U.S. patent application number 15/641284 was filed with the patent office on 2018-02-15 for face detection and recognition.
The applicant listed for this patent is APPLIED RECOGNITION INC.. Invention is credited to RAY GANONG, YONG MAN RO, KONSTANTINOS PLATANIOTIS, CHRIS STUDHOLME, DONALD CRAIG WAUGH.
Application Number | 20180046855 15/641284 |
Document ID | / |
Family ID | 40823725 |
Filed Date | 2018-02-15 |
United States Patent
Application |
20180046855 |
Kind Code |
A1 |
GANONG; RAY ; et
al. |
February 15, 2018 |
FACE DETECTION AND RECOGNITION
Abstract
The present invention provides, in at least one aspect, methods
and systems that detect at least one face in at least one digital
image, determine and store area co-ordinates of a location of the
at least one detected face in the at least one digital image, apply
at least one transformation to the at least one detected face to
create at least one portrait of the at least one detected face,
rotate the at least one portrait at least until the at least one
portrait is shown in a vertical orientation and a pair of eyes of
the at least one face shown in the at least one portrait are
positioned on a horizontal plane; and store the rotated at least
one portrait.
Inventors: |
GANONG; RAY; (BURLINGTON,
CA) ; WAUGH; DONALD CRAIG; (OAKVILLE, CA) ;
MAN RO; YONG; (DAEJEON, KR) ; PLATANIOTIS;
KONSTANTINOS; (TORONTO, CA) ; STUDHOLME; CHRIS;
(OTTAWA, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
APPLIED RECOGNITION INC. |
BURLINGTON |
|
CA |
|
|
Family ID: |
40823725 |
Appl. No.: |
15/641284 |
Filed: |
July 4, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15036313 |
May 12, 2016 |
9721148 |
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PCT/CA2014/000802 |
Nov 12, 2014 |
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15641284 |
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14078071 |
Nov 12, 2013 |
9639740 |
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15036313 |
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12811240 |
Jun 30, 2010 |
8750574 |
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PCT/CA08/02276 |
Dec 30, 2008 |
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14078071 |
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61017895 |
Dec 31, 2007 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06K 9/00248 20130101;
G06K 9/42 20130101; G06F 16/955 20190101; G06K 9/00281 20130101;
G06K 9/00288 20130101; G06Q 30/02 20130101; G06K 9/00677 20130101;
G06F 16/583 20190101; G06F 15/16 20130101; G06Q 30/0269 20130101;
G06K 9/4652 20130101; G06Q 50/01 20130101; G06F 16/50 20190101 |
International
Class: |
G06K 9/00 20060101
G06K009/00 |
Claims
1. A method performed by at least one computer, the method
comprising: detecting at least one face in at least one digital
image; determining and storing area co-ordinates of a location of
the at least one detected face in the at least one digital image;
applying at least one transformation to the at least one detected
face to create at least one unidentified portrait of the at least
one detected face; comparing a face signature of the at least one
unidentified portrait against a plurality of face signatures of
identified portraits, each identified portrait associated with an
identified person shown in the respective portrait; in accordance
with a positive comparison, associating the at least one
unidentified portrait with the at least one identified person; and
storing the at least one unidentified portrait.
2. The method of claim 1 wherein each of the at least one detected
face corresponds to a person.
3. The method of claim 1 comprising extracting the at least one
detected face from the at least one digital image prior to the
applying.
4. The method of claim 1 comprising inserting the at least one
stored portrait in a marketing image.
5. The method of claim 1 wherein the at least one digital image is
from a frame of a video having a plurality of frames.
6. The method of claim 4 comprising applying at least one
additional transformation to the at least one stored portrait for
matching a portrait size requirement of the marketing image.
7. The method of claim 1 comprising: receiving a selection of at
least one face to be suppressed; masking the area co-ordinates
corresponding to the location of the at least one face to be
suppressed in the at least one digital image.
8. The method of claim 7 wherein the masking comprises overlaying a
selected image over the area co-ordinate corresponding to the
location of the at least one face to be suppressed.
9. The method of claim 8 comprising adjusting the selected image to
correspond to size of the area co-ordinates to be masked.
10. The method of claim 8 comprising adjusting edges of the
overlayed image to blend with image characteristics of the at least
one digital image.
11. The method of claim 10 wherein the image characteristics may
include one or more of color, intensity, brightness, and
texture.
12. The method of claim 7 wherein the selection of the at least one
face to be suppressed comprises a command to suppress a particular
individual from being displayed in any digital image, the method
comprising identifying any digital image comprising a face
corresponding to the particular individual to be suppressed and
flagging the identified digital images to mask the area
co-ordinates corresponding to the location of the at least one face
to be suppressed.
13. The method of claim 7 wherein the masking comprises applying a
permanent mask to the digital image.
14. The method of claim 7 wherein the masking comprises modifying
metadata of the digital image to cause the digital image to be
masked when displayed.
15. The method of claim 1 comprising associating date data with the
digital image; and displaying stored portraits corresponding to a
common person in accordance with the respective associated date
data.
16. The method of claim 15 comprising generating an animated
representation of an evolution of the stored portraits
corresponding to the common person over time.
17. The method of claim 1 comprising associating an identification
of a person with the at least one stored portrait.
18. The method of claim 17 comprising associating relationships
between identified persons based at least partly on respective
identified persons being included in the digital image.
19. A system comprising at least one computer comprising at least
one processor and a non-transitory computer readable memory
comprising processing instructions, that when executed by the at
least one processor, cause the computer to: detect at least one
face in at least one digital image; determine and store area
co-ordinates of a location of the at least one detected face in the
at least one digital image; apply at least one transformation to
the at least one detected face to create at least one unidentified
portrait of the at least one detected face; compare a face
signature of the at least one unidentified portrait against a
plurality of face signatures of identified portraits, each
identified portrait associated with an identified person shown in
the respective portrait; in accordance with a positive comparison,
associate the at least one unidentified portrait with the at least
one identified person; and store the rotated at least one
portrait.
20. The system of claim 19 wherein the at least one digital image
is from a frame of a video having a plurality of frames.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 15/036,313 filed Nov. 12, 2014 which is a
national phase entry of International application serial no.
PCT/CA2014/000802 filed Nov. 12, 2014 which is a continuation of
and claims priority from U.S. patent application Ser. No.
14/078,071, filed Nov. 12, 2013 entitled "FACE DETECTION AND
RECOGNITION", which is a continuation-in-part of U.S. patent
application Ser. No. 12/811,240 filed Jun. 30, 2010, which is a
national phase entry of International application serial no.
PCT/CA2008/02276 filed Dec. 30, 2008, which claims the benefit of
U.S. provisional patent application Ser. No. 61/017,895 filed Dec.
31, 2007, the entire contents of all of which are hereby
incorporated by reference in entirety.
FIELD OF THE INVENTION
[0002] The present invention relates to face and portrait
extraction using face detection and recognition and application
thereof.
BACKGROUND OF THE INVENTION
[0003] Social networks presently exist as a means to connect
individuals using a website. The following definition exists in the
PCMAG.COM.TM. encyclopedia for "social network": [0004] An
association of people drawn together by family, work or hobby. The
term was first coined by Professor J. A. Barnes in the 1950s, who
defined the size of a social network as a group of about 100 to 150
people.
[0005] The following definition exists in the PCMAG.COM.TM.
encyclopedia for "social network site": [0006] A Web site that
provides a virtual community for people interested in a particular
subject or just to "hang out" together. Members communicate by
voice, chat, instant message, videoconference and blogs, and the
service typically provides a way for members to contact friends of
other members. Such sites may also serve as a vehicle for meeting
in person. The "social networking site" is the 21st century term
for "virtual community," a group of people who use the Internet to
communicate with each other about anything and everything. [0007]
Friendster (www.friendster.com) was the first social networking
site, which was introduced in 2002 and followed by MySpace
(www.myspace.com) a year later. Started by two friends, MySpace
became extremely popular, and its parent company, Intermix, was
acquired by News Corporation for $580 million two years after
MySpace was launched. [0008] Facebook (www.facebook.com) came out
in 2004 initially targeting college students, but later welcoming
everyone. Following Facebook were TagWorld (www.tagworld.com) and
Tagged (www.tagged.com). TagWorld introduced tools for creating
more personalized Web pages, and Tagged introduced the concept of
building tag teams for teens with like interests. [0009] Social
networking sites compete for attention much like the first Web
portals when the Internet exploded onto the scene in the mid-1990s.
Many variations are expected.
[0010] Many social network sites allow users to upload and share
photos. Some also incorporate a feature for tagging photos to
identify the names of people (faces) in the photos (FACEBOOK.TM.,
for example, provides this feature). Based on user surveys the
majority of respondents state that the tagging effort is manual and
very time consuming. Also, for privacy reasons, many users do not
upload all of their digital photos to the sharing website. It is
also very time consuming and bandwidth intensive to upload
thousands of photos. So while a user of a social network site may
have 10,000 digital photos on their local computer, they only
upload one or two hundred to share with their friends. This is
based on user surveys conducted by Applied Recognition Inc. in
September 2007.
[0011] There are also websites that allow registered users to
upload digital photos and digital video and store them on a website
for sharing purposes. These are dedicated to this purpose. Examples
of these sites include FLICKR.TM. and PHOTOBUCKET.TM.. The drawback
with these sites is that all tagging of photos to identify friends
is manual and time consuming; PHOTOBUCKET.TM. does not allow people
tagging in a photo. With FLICKR.TM., if an average photo contains
two people, then it may take 10-15 seconds per photo to tag the
people. When that time is multiplied by 100 or 1000, it becomes too
time-consuming and the average person just will not perform
tagging.
[0012] Rapid growth in photo-taking devices is occurring today with
the incorporation of digital cameras in most modern cell phones. In
fact, more cameras are sold via cell phones today than all
dedicated digital cameras combined. This is causing a proliferation
in the number of digital images that are uploaded and stored on
home computers. Because the average number of digital images
exceeds 1000 based on our surveys, the effort is too great to
manually tag and properly organize the images for the majority of
people.
[0013] One company, RIYA.TM. (www.riya.com), created a product that
is web-based for identifying faces in digital photos. This product
involved the download of a software module for identifying faces in
photos on the user's local computer before uploading these photos
to the remote RIYA.TM. web-based server where the faces were
compared with other faces to find matches. This product is a
prototype and as such has no automatic photo sharing features based
on recognition. It also has no features for enabling the user to
correct the inevitable errors that occur in any automated face
detection and recognition method.
[0014] Prior to 2007, image searches were conducted using text or
dates related to the photo such as captions, titles, description,
creation date, etc., as opposed to the image content itself. Since
then there have been a number of companies that began introducing
face detection and recognition in consumer photo applications
including: in 2008, Google introduced face recognition into Picasa;
Polar Rose application for Flickr in 2009; Apple purchased Polar
Rose in 2010 and introduced the feature in iPhoto; Microsoft
introduced face recognition their Photo Gallery product in 2010;
Facebook introduced face detection in 2010; and in 2010 Sony
Ericsson integrated face-recognition software into its photo
gallery.
SUMMARY OF THE INVENTION
[0015] In one aspect of the present invention, a method for
recognizing one or more faces in a digital image is provided, the
method characterized by: (a) generating one or more face
coordinates corresponding to one or more candidate regions for one
or more faces; (b) generating eye coordinates based on the face
coordinates; (c) detecting each face using one or more projection
images defined by the face coordinates and the eye coordinates; and
(d) comparing each projection image with one or more known
projection images, wherein a similarity threshold is provided for
defining a best match between the projection image and the known
projection images.
[0016] In another aspect of the present invention, a method for
sharing a digital image depicting one or more faces is provided,
the method characterized by: (a) linking a plurality of computer
terminals to a computer network, each computer terminal associated
with an individual; (b) linking the digital image to at least one
of the computer terminals; (c) enabling at least one of the
computer terminals to initiate a face recognition routine on the
digital image, the face recognition routine producing a list of one
or more persons whose faces are depicted in the digital image, at
least one of the persons being one of the individuals; and (d)
enabling at least one of the computer terminals to initiate a
sharing routine for disseminating the digital image to the computer
terminals associated with the one or more persons.
[0017] In another aspect of the present invention, a method for
sharing a digital image depicting one or more faces is provided,
the method characterized by: (a) linking a plurality of computer
terminals to a computer network and a plurality of cloud services
with the digital images and metadata stored in a cloud-based data
repository. (b) linking the digital image to at least one of the
computer terminals; (c) enabling at least one of the computer
terminals to initiate a face recognition routine on the digital
image, the face recognition routine producing a list of one or more
persons whose faces are depicted in the digital image, at least one
of the persons being one of the individuals; and (d) enabling at
least one of the computer terminals to initiate a sharing routine
for disseminating the digital image to the computer terminals
associated with the one or more persons.
[0018] In another aspect of the present invention, a method for
sharing a digital image depicting one or more faces is provided,
the method characterized by: (a) linking a plurality of computer
terminals to a computer network, each computer terminal associated
with an individual and a plurality of cloud services with the
digital images and metadata stored on each computer terminal and in
a cloud-based data repository. (b) linking the digital image to at
least one of the computer terminals; (c) enabling at least one of
the computer terminals to initiate a face recognition routine on
the digital image, the face recognition routine producing a list of
one or more persons whose faces are depicted in the digital image,
at least one of the persons being one of the individuals; and (d)
enabling at least one of the computer terminals to initiate a
sharing routine for disseminating the digital image to the computer
terminals associated with the one or more persons.
[0019] In another aspect of the present invention, a method for
sharing a digital image depicting one or more faces is provided,
the method characterized by: (a) linking a plurality of a cameras
or smart phones with cameras taking a picture or taking video
images and linking a plurality of computer terminals to a computer
network, each computer terminal associated with an individual and a
plurality of cloud services with the digital images and metadata
stored on each computer terminal and in a cloud-based data
repository. (b) linking the digital image to at least one of the
computer terminals; (c) enabling the cameras or smart phones with
cameras to initiate a face recognition routine on the digital
image, the face recognition routine producing a list of one or more
persons whose faces are depicted in the digital image, at least one
of the persons being one of the individuals; and (d) enabling at
least one of the computer terminals to initiate a sharing routine
for disseminating the digital image to the computer terminals
associated with the one or more persons.
[0020] In a further aspect of the present invention, a system for
recognizing one or more faces in a digital image is provided, the
system characterized by: (a) one or more face coordinates
corresponding to one or more candidate regions for one or more
faces; (b) eye coordinates generated based on the face coordinates;
(c) one or more projection images defined by the face coordinates
and the eye coordinates; and (d) a similarity threshold for
defining a best match between each projection image and one or more
known projection images, the best match determining an identity
corresponding to each of the one or more faces.
[0021] In a still further aspect of the present invention, a system
for sharing a digital image depicting one or more faces is
provided, the system characterized by: (a) a plurality of computer
terminals linked to a computer network, each computer terminal
associated with an individual; (b) a digital image operable to be
linked to at least one of the computer terminals; (c) a face
recognition routine operable to be initiated by at least one of the
computer terminals, the face recognition routine producing a list
of one or more persons whose faces are depicted in the digital
image, at least one of the persons being one of the individuals;
and (d) a sharing routine operable to be initiated by at least one
of the computer terminals, the sharing routine disseminating the
digital image to the computer terminals associated with the one or
more persons.
[0022] In yet another aspect of the present invention, a method for
providing secure targeted advertising is provided, the method
characterized by: (a) tracking one or more demographic properties
associated with an individual registered to a computer program; (b)
retrieving from a first source a list of advertising pointers
associated with one or more advertisements targeted based on the
one or more demographic properties; (c) retrieving from a second
source the one or more advertisements; (d) deleting the one or more
demographic properties from the first source; and (e) presenting
the advertisements to the individual.
[0023] In a further aspect of the present invention, a system for
recognizing one or more faces in a digital image is provided, the
system characterized by: (a) one or more face coordinates
corresponding to one or more candidate regions for one or more
faces; (b) eye coordinates generated based on the face coordinates;
(c) one or more projection images defined by the face coordinates
and the eye coordinates; and (d) a similarity threshold for
defining a best match between each projection image and one or more
known projection images, the best match determining an identity
corresponding to each of the one or more faces.
[0024] In a still further aspect of the present invention, a system
for sharing a digital image depicting one or more faces is
provided, the system characterized by: (a) a plurality of computer
terminals linked to a computer network, each computer terminal
associated with an individual; (b) a digital image operable to be
linked to at least one of the computer terminals; (c) a face
recognition routine operable to be initiated by at least one of the
computer terminals, the face recognition routine producing a list
of one or more persons whose faces are depicted in the digital
image, at least one of the persons being one of the individuals;
and (d) a sharing routine operable to be initiated by at least one
of the computer terminals, the sharing routine disseminating the
digital image to the computer terminals associated with the one or
more persons.
[0025] In yet a further aspect of the present invention, a system
for providing secure targeted advertising is provided, the system
characterized by: (a) one or more demographic properties associated
with an individual registered to a computer program; (b) a first
source operable to provide a list of advertising pointers
associated with one or more advertisements targeted based on the
one or more demographic properties; (c) a second source operable to
provide the one or more advertisements; (d) a means for deleting
the one or more demographic properties from the first source; and
(e) a means for presenting the advertisements to the
individual.
[0026] In a further still aspect of the present invention, a
computer program product for recognizing one or more faces in a
digital image is provided, the computer program product
characterized by: (a) a computer readable medium including software
instructions; and (b) the software instructions for enabling the
computer to perform predetermined operations, the predetermined
operations including the steps of: (i) generating one or more face
coordinates corresponding to one or more candidate regions for one
or more faces; (ii) generating eye coordinates based on the face
coordinates; (iii) detecting each face using one or more projection
images defined by the face coordinates and the eye coordinates; and
(iv) comparing each projection image with one or more known
projection images, wherein a similarity threshold is provided for
defining a best match between the projection image and the known
projection images.
[0027] In another aspect of the present invention, a computer
program product for sharing a digital image depicting one or more
faces is provided, the computer program product characterized by:
(a) a computer readable medium including software instructions; and
(b) the software instructions for enabling the computer to perform
predetermined operations, the predetermined operations including
the steps of: (i) linking a plurality of computer terminals to a
computer network, each computer terminal associated with an
individual; (ii) linking the digital image to at least one of the
computer terminals; (iii) enabling at least one of the computer
terminals to initiate a face recognition routine on the digital
image, the face recognition routine producing a list of one or more
persons whose faces are depicted in the digital image, at least one
of the persons being one of the individuals; and (iv) enabling at
least one of the computer terminals to initiate a sharing routine
for disseminating the digital image to the computer terminals
associated with the one or more persons.
[0028] In yet another aspect of the present invention, a computer
program product for providing secure targeted advertising is
provided, the computer program product characterized by: (a) a
computer readable medium including software instructions; and (b)
the software instructions for enabling the computer to perform
predetermined operations, the predetermined operations including
the steps of: (i) tracking one or more demographic properties
associated with an individual registered to a computer program;
(ii) retrieving from a first source a list of advertising pointers
associated with one or more advertisements targeted based on the
one or more demographic properties; (iii) retrieving from a second
source the one or more advertisements; (iv) deleting the one or
more demographic properties from the first source; and (v)
presenting the advertisements to the individual.
[0029] The present invention, in a further aspect thereof, enables
capturing portraits of people whose faces are located in an image.
If faces are detected in an image, the computer program herein
provided captures the XY coordinates of the location of the face
and records this in the database. The computer program may be
configured to adjust the size of the face region in order to
capture and create a portrait (or thumbnail) of the person.
Furthermore the computer program provided herein may automatically
adjust the rotation of the face to render the face position
vertically for the portrait with eyes on a horizontal plane.
[0030] The present invention, in a further aspect thereof, enables
the utilization of portraits captured and stored in a database of
people whose faces are located in an image. The computer program
may select and merge a portrait with an advertisement or a product
image for display to the consumer. The computer program may be
configured to adjust the size of the portrait to match the size
requirements of the advertisement or product image
[0031] The present invention, in a further aspect thereof, enables
hiding of people whose faces are located in an image. The computer
program queries the database for the XY coordinates of the location
of the face to be suppressed. The user of the computer program may
select an image that is used to mask the existing face region. The
computer program will overlay the selected image over the face of
the selected person--in a single image or all images containing
that person--and display the modified image to the consumer. This
action may be taken for entertainment purposes or to hide negative
memories. The computer program may be configured to adjust the size
of the portrait to match the size requirements of the selected face
size to hide. The computer program may store the modifications in
metadata or apply the changes permanently to the original photo.
The edges of the overlayed (replacement) image may be adjusted to
match color, intensity, brightness, texture and other
characteristics to blend into the original image and be more
visually appealing.
[0032] Yet another aspect of the present invention enables the
display of faces or portraits captured from photos in historical
order. The computer program enables the capturing of portraits from
images. The computer also tracks the date of the photo either from
file information, EXIF (metadata) information or the user of the
computer program may optionally specify a date for an image such as
is required for scans of older non-digital photos. The computer
program employs date data from photos correlated to portraits
captured from such photos to arrange the display of the portraits
according to date. The computer program can subsequently use photo
morphing techniques to generate and display an animation the
evolution of a person's face over time.
[0033] Yet another aspect of the present invention enables the
faces or portraits captured from photos to be utilized as content
for games and exercises. The computer program enables the capturing
of portraits from images. The computer program also maintains a
database of people in the photos and the relationships between
people. The computer program, using such data, correlates the user
of the program ("the player") with photos and portraits captured
from such photos to arrange the display of said photos and
portraits customized based on the player of the game or exercise.
Such customization could be a simple as a slide show of the photos
based on the relationship. The customization could also be games
such as those useful for people with Alzheimer's or Dementia where
personal photos and relationships can be presented to the Alzheimer
patient as a game or exercise. This category of treatment is
generally known as photo reminiscence therapy. The computer program
using the index of photos and portraits extracted from photos
retrieve such photos and portraits based on database queries and
embeds selected images into games and exercises.
[0034] The present invention, in a further aspect thereof,
facilitates the creation of a family tree as an index for the
photos. The computer program queries the database for all portraits
related to a given user and then presents the same to the user. The
user of the computer program then selects the portraits and drags
them on to a specific node in the family tree to add a new
relationship. The computer program will dynamically spawn
additional relationships as the user drags new portraits of
relatives on to the family tree diagram. Once all portraits for a
given family have been added to the family tree it is complete and
then becomes a dynamic index for the consumer to display and locate
photos. Optionally the same tree format can be used to connect
friends together and show the (self-defined) relationships between
different categories of friends. Example would be co-workers, high
school friends, college friends, etc.
[0035] The present invention, in a further aspect thereof enables
the use of group portraits to act as an index for the photos. This
is of particular interest to any organization of people. Typically
organizations maintain group photos by year. For example a sports
team will have a team photo displaying the players for every
season. A scout troop, orchestra, class photo, club etc. will
typically use the same approach. The computer program enables the
use of group photos and the resultant identification of subjects in
said photos to act as an index to photos containing individuals
within the group. This enables the organization and structuring of
photo displays based on year/season as well as by group members,
and group member subsets.
[0036] Yet another aspect of the present invention enables the
displays of faces or portraits of missing children, FBI most wanted
and similar requirements to find a person. The computer program
displays these images to users of the computer program seeking the
assistance of users to find the said persons. The computer also
delivers the face signatures of the said person being sought and
requests the users permission to compare said face signature with
their personal database and report potential matches. Should there
be a potential match the user will be presented with such potential
match and can optionally notify the respective authority of such
potential. This could enable the organization to determine a recent
or current location of the individual. It could also enable the
organization to determine the name of an individual, since the
individual's name may be listed in one or more of the user's known
persons list.
[0037] In accordance with an aspect of the present invention, there
is provided a method performed by at least one computer, the method
comprising: detecting at least one face in at least one digital
image; determining and storing area co-ordinates of a location of
the at least one detected face in the at least one digital image;
applying at least one transformation to the at least one detected
face to create at least one portrait of the at least one detected
face; rotating the at least one portrait at least until the at
least one portrait is shown in a vertical orientation and a pair of
eyes of the at least one face shown in the at least one portrait
are positioned on a horizontal plane; and storing the rotated at
least one portrait.
[0038] In accordance with an aspect of the present invention, there
is provided a system comprising at least one computer comprising at
least one processor and a non-transitory computer readable memory
comprising processing instructions, that when executed by the at
least one processor, cause the computer to: detect at least one
face in at least one digital image; determine and store area
co-ordinates of a location of the at least one detected face in the
at least one digital image; apply at least one transformation to
the at least one detected face to create at least one portrait of
the at least one detected face; rotate the at least one portrait at
least until the at least one portrait is shown in a vertical
orientation and a pair of eyes of the at least one face shown in
the at least one portrait are positioned on a horizontal plane; and
store the rotated at least one portrait.
[0039] In accordance with an aspect of the present invention, there
is provided a method performed by at least one computer, the at
least one computer comprising or interfacing with a database, the
database comprising a plurality of portraits, each portrait
associated with an identified person shown in the respective
portrait, the method comprising: displaying the respective portrait
of at least one identified person associated with a user;
displaying a visual representation of at least one personal
relationship to the user; assigning at least one of the displayed
portraits to at least one of the displayed personal relationships,
in accordance with a received user input; and storing the personal
relationship assignments in the database.
[0040] In accordance with an aspect of the present invention, there
is provided a method performed by at least one computer, the at
least one computer comprising or interfacing with a database of
portraits and digital images, each portrait associated with an
identified person shown in the respective portrait, the method
comprising: displaying at least one of the digital images;
cross-referencing the displayed at least one digital image with the
database of portraits to create a list of at least one identified
person shown in the at least one digital image; indexing at least a
subset of the digital images in accordance with the list of at
least one identified person; and in accordance with a user input
selecting at least one of the identified persons from the displayed
at least one digital image, displaying at least one of the
respectively indexed digital images.
[0041] In accordance with an aspect of the present invention, there
is provided a method performed by at least one computer, the at
least one computer comprising or interfacing with a database of
portraits, each portrait associated with an identified person shown
in the respective portrait, the method comprising: receiving at
least one unidentified portrait; comparing face signature of the at
least one unidentified portrait against face signatures of
portraits of identified persons known to the user; in accordance
with a positive result of the comparing, prompting the user for
confirmation of the positive result; and in accordance with the
confirmation, associating the at least one unidentified portrait
with the at least one identified person confirmed by the user and
storing the at least one unidentified portrait in the database.
[0042] In accordance with an aspect of the present invention, there
is provided a method performed by at least one computer, the at
least one computer comprising or interfacing with a database of
digital images and respective metadata identifying a name of at
least one identified person shown in the respective digital image,
the method comprising: selecting one of the identified persons from
the database; determining a count of a total number of digital
images where the identified person appears; for each identified
person shown in at least one of the digital images with the
selected identified person, determining a count of a total number
of digital images where the respective identified person appears
with the selected identified person; and displaying a visual
representation comprising: a first node representing the selected
identified person and the respective count of the total number of
digital images where the identified person appears; for each
identified person shown in at least one of the digital images with
the selected identified person, a second node representing the
respective identified person shown in at least one of the digital
images with the selected identified person, each respective node
further comprising a visual representation of the respective count
of the total number of digital images where the respective
identified person appears with the selected identified person; and
a link between the first node and each second node.
[0043] In accordance with an aspect of the present invention, there
is provided a method performed by at least one computer, the at
least one computer comprising or interfacing with a database of
digital images and respective metadata identifying a name of at
least one identified person shown in the respective digital image,
the method comprising: selecting one of the identified persons from
the database; determining at least one first identified person
shown in at least one of the digital images together with the
selected identified person; and displaying a visual representation
comprising: for each first identified person, a first tier node
representing the selected identified person and the respective
first identified person being shown in at least one of the digital
images together; and for each first identified person, a second
tier node representing the respective first identified person being
shown in at least one of the digital images without the selected
identified person.
[0044] In accordance with an aspect of the present invention, there
is provided a method performed by at least one computer, the at
least one computer comprising or interfacing with a database of
portraits, each portrait associated with an identified person shown
in the respective portrait, the method comprising: selecting one of
the identified persons from the database; ordering a plurality of
the portraits associated with the selected identified person based
at least partly on date metadata associated with each respective
portrait; displaying a visual representation comprising: a
timeline; and an arrangement of the plurality of the portraits
along the timeline in accordance with the respective ordering.
[0045] In accordance with an aspect of the present invention, there
is provided a method performed by at least one computer comprising
or interfacing with a database of digital images and respective
metadata identifying a date of the respective digital image, a
plurality of the digital images showing at least one respective
unidentified person ("unidentified digital images"), the method
comprising: sorting the unidentified digital images by the
respective date metadata; assigning a respective clustering token
to each of the unidentified digital images, wherein the assigning
comprises, in accordance with a determination that a subset of the
unidentified digital images each show a common unidentified person,
assigning a common respective clustering token to each of the
unidentified digital images of the subset; grouping the
unidentified digital images by respective clustering token;
receiving a new digital image and respective metadata identifying a
date of the respective new digital image, the new digital image
comprising a new unidentified person; performing at least one
comparison of the new unidentified person to the at least one
respective unidentified person of the plurality of the digital
images in an order, wherein for each group of unidentified digital
images, performing only a single comparison of the new unidentified
person to the respective common unidentified person; and assigning
a clustering token to the new digital image in accordance with the
comparison performing resulting in a determination of the new
unidentified person common to a respective one of the groups of
unidentified digital images.
[0046] In accordance with an aspect of the present invention, there
is provided a method of suggesting an identification of an
unidentified person in a received digital image, the method
performed by at least one computer comprising or interfacing with a
database of portraits and associated digital images, each portrait
associated with an identified person shown in the respective
portrait, the method comprising: determining that respective
portraits of at least two identified persons shown in one of the
digital images satisfy a comparison threshold with a portrait of
the unidentified person determined from the received digital image;
suggesting an identification of the unidentified person as the
respective one of the at least two identified persons having a
respectively associated portrait that is determined to be a closest
match to the portrait of the unidentified person from amongst the
at least two identified persons; and excluding a remainder of the
at least two identified persons from being subsequently suggested
from any other one of the digital images as an identification of
the unidentified person.
[0047] In accordance with an aspect of the present invention, there
is provided a method of suggesting an identification of an
unidentified person in a received digital image, the method
performed by at least one computer comprising or interfacing with a
database of portraits and associated digital images, each portrait
associated with an identified person shown in the respective
portrait, the method comprising: determining that a respective
portrait of at least one identified person shown in at least one of
the digital images satisfies a comparison threshold with a portrait
of the unidentified person determined from the received digital
image; in accordance with the determined at least one of the
digital images associated with metadata comprising a date
corresponding to date metadata associated with the received digital
image, suggesting an identification of the unidentified person as
the at least one identified person.
[0048] In accordance with an aspect of the present invention, there
is provided a method of suggesting an identification of an
unidentified person in a received digital image, the method
performed by at least one computer comprising or interfacing with a
database of portraits and associated digital images, each portrait
associated with an identified person shown in the respective
portrait, the method comprising: in accordance with a determination
that the received photo comprises at least one identified person
associated with a defined group, suggesting an identification of
the unidentified person based at least partly on a determination
that a respective portrait of at least one identified person
associated with the defined group satisfies a comparison threshold
with a portrait of the unidentified person determined from the
received digital image.
[0049] In accordance with an aspect of the present invention, there
is provided a method of suggesting an identification of an
unidentified person in a received digital image, the method
performed by at least one computer comprising or interfacing with a
database of portraits and associated digital images, each portrait
associated with an identified person shown in the respective
portrait, the method comprising: determining that a respective
portrait of at least one identified person shown in at least one of
the digital images satisfies a comparison threshold with a portrait
of the unidentified person determined from the received digital
image; in accordance with the determined at least one of the
digital images associated with metadata comprising: a date
corresponding to date metadata associated with the received digital
image; and a location within a predetermined distance threshold of
location metadata associated with the received digital image;
suggesting an identification of the unidentified person as the at
least one identified person.
[0050] In accordance with an aspect of the present invention, there
is provided a method of suggesting an identification of an
unidentified person in a received digital image, the method
performed by at least one computer comprising or interfacing with a
database of portraits and associated digital images, each portrait
associated with an identified person shown in the respective
portrait, the method comprising: determining that a respective
portrait of at least one identified person shown in at least one of
the digital images satisfies a comparison threshold with a portrait
of the unidentified person determined from the received digital
image; determining that the received digital image is associated
with an event, based at least partly on metadata associated with
the received digital image; in accordance with the determined at
least one of the digital images being associated with the event,
suggesting an identification of the unidentified person as the at
least one identified person.
[0051] In accordance with an aspect of the present invention, there
is provided a method of suggesting an identification of an
unidentified person in a received digital image, the method
performed by at least one computer comprising or interfacing with a
database of portraits and associated digital images, each portrait
associated with an identified person shown in the respective
portrait, the method comprising: determining that a respective
portrait of at least one identified person shown in at least one of
the digital images satisfies a comparison threshold with a portrait
of the unidentified person determined from the received digital
image; extracting non-portrait visual information from the
determined at least one of the digital images; in accordance with a
determination of the extracted non-portrait visual information
satisfying a comparison threshold with non-portrait visual
information from the received digital image, suggesting an
identification of the unidentified person as the at least one
identified person.
[0052] In this respect, before explaining at least one embodiment
of the invention in detail, it is to be understood that the
invention is not limited in its application to the details of
construction and to the arrangements of the components set forth in
the following description or illustrated in the drawings. The
invention is capable of other embodiments and of being practiced
and carried out in various ways. Also, it is to be understood that
the phraseology and terminology employed herein are for the purpose
of description and should not be regarded as limiting.
BRIEF DESCRIPTION OF THE DRAWINGS
[0053] Embodiments will now be described, by way of example only,
with reference to the attached figures, wherein:
[0054] FIG. 1 illustrates a particular embodiment of the system of
the present invention incorporating a social network service to
perform targeted distribution of photos.
[0055] FIG. 2 further illustrates the system illustrated in FIG. 1,
wherein users add new digital images from various devices over
time.
[0056] FIG. 3 illustrates the face recognition method of the
present invention, in one aspect thereof, for generating face
"signatures" that are compared with signatures of known
persons.
[0057] FIG. 4 illustrates linking the results of a face detection,
eye detection and face recognition technique in a face database on
the storage device of the local computer system.
[0058] FIG. 5 illustrates how peer groups can grow and evolve over
time as the list of known persons grows.
[0059] FIG. 6 illustrates potential methods of correcting errors
that may result from the automatic face detection, eye detection,
and face recognition steps.
[0060] FIG. 7 illustrates a system and method for providing the
automatic selective dissemination of photos between users of the
invention in the same peer group.
[0061] FIG. 8 illustrates an example embodiment of the graphical
user interface that may enable browsing of photos and the face
database managed by the computer program.
[0062] FIG. 9 shows face images for known persons plus Boolean
operators to narrow the field of photos.
[0063] FIG. 10 illustrates an optional advertising display
capability provided by the GUI.
[0064] FIG. 11 illustrates the texture-based face detection method
of the present invention, in one aspect thereof.
[0065] FIG. 12 illustrates a method for eye detection, in one
aspect of the present invention.
[0066] FIG. 13 illustrates the face recognition method, in one
aspect of the present invention.
[0067] FIG. 13a illustrates the face recognition method, in one
aspect of the present invention.
[0068] FIGS. 14A, 14B, and 14C illustrate a method of isolating
eyes in a photo.
[0069] FIG. 15 illustrates an example configuration of the system
of the present invention.
[0070] FIG. 16 illustrates an interface for enabling a user to
confirm the identity of a face appearing in an image.
[0071] FIG. 17 illustrates a means by which a user may delete false
positive face detections in an image.
[0072] FIG. 18 illustrates a means by which a user may reposition
detected eye coordinates corresponding to a face in an image for
the purpose of increasing accuracy of the detection algorithm.
[0073] FIG. 19 illustrates the process of manually tagging a face
in an image.
[0074] FIG. 20 illustrates a video scanning method whereby frames
of video are extracted and face detection is performed on these
frames.
[0075] FIG. 21 illustrates one aspect of the present invention
wherein a remote web browser or mobile device is enabled to access
a proxy server, providing a connection to the functions of the
present invention.
[0076] FIG. 22 illustrates deletion of a false positive face
detection error.
[0077] FIG. 23 illustrates one aspect of the present invention
wherein faces are located in an image which captures the xy
coordinates of the location of the faces providing a method to
capture a face and adjust the image of the face both in terms of
size and rotation to create a portrait--either full size or
thumbnail.
[0078] FIG. 24 illustrates copying the image of the face from a
photo adjusted in terms of size and rotation and embedding the same
in to an advertising message or in to a photo of a product.
[0079] FIG. 25 illustrates using an image to overlay on a photo
over the face of a subject to hide negative memories.
[0080] FIG. 26 illustrates the display of faces captured from
photos in historical order.
[0081] FIG. 27 illustrates the display of faces/portraits captured
from photos and embedding the same in to games and exercises.
[0082] FIG. 28 illustrates the creation and use of a family tree to
be an index for photos.
[0083] FIG. 29 illustrates the use of group photos to be an index
for photos.
[0084] FIG. 30 illustrates the displays the delivery of face
signatures of missing children and wanted criminals.
[0085] FIG. 31 illustrates the matching of face signatures to find
people who look alike.
[0086] FIG. 32 illustrates the workflow to create a customized
advertisement.
[0087] FIG. 33 illustrates the workflow to create a customized
product.
[0088] FIG. 34 illustrates the workflow to hide a negative
memory.
[0089] FIG. 35 illustrates the workflow to create customized games
and exercises.
[0090] FIG. 36 illustrates the workflow to create a family
tree.
[0091] FIG. 37 illustrates the workflow to use a photo as an
index.
[0092] FIG. 38 illustrates the workflow to find a missing
person.
[0093] FIG. 39 illustrates the workflow to find a look alike.
[0094] FIGS. 40-45 illustrate workflows in accordance with aspects
of the invention to use metadata to enhance face recognition
results.
[0095] FIG. 46 illustrates a relationship diagram showing frequency
of identified persons appearance together, in accordance with an
aspect of the present invention.
[0096] FIG. 47 illustrates displaying photos in a tiered list
format in accordance with an aspect of the present invention.
[0097] FIG. 48 illustrates a collage of photos representing the
tiers shown in FIG. 48.
[0098] FIG. 49 illustrates displaying a timeline of face portraits
for an identified person, in accordance with an aspect of the
present invention.
[0099] FIG. 50 illustrates identifying a face in a photo by using
clustering in accordance with an aspect of the present
invention.
[0100] FIGS. 51-53 illustrate face grouping and clustering in
accordance with an aspect of the present invention.
[0101] In the drawings, embodiments of the invention are
illustrated by way of example. It is to be expressly understood
that the description and drawings are only for the purpose of
illustration and as an aid to understanding, and are not intended
as a definition of the limits of the invention.
DETAILED DESCRIPTION
Overview
[0102] The present invention, in one aspect thereof, provides a
networked computer architecture enabling the automatic distribution
of images relating to a plurality of individuals operating computer
systems on the network.
[0103] The present invention, in another aspect thereof, provides a
computer program operable to enable each of the individuals to
interface with the networked computer architecture herein provided
for sharing information including images. The computer program
enables the individuals to upload images including images having
depictions of the faces of one or more persons. The computer
program may perform a face detection technique to detect the one or
more faces in the image, which may result in the generation of one
or more face signatures, each face signature corresponding to one
of the faces. The computer program may then access a database,
wherein the database links face signatures with a list of known
persons, each known person being associated with one or more face
signatures. Each detected face signature may be provided to the
individual as associated to the corresponding known person, or
where the face signature is not associated with any known person,
that information can be provided by the individual. The individual
may be provided a means to confirm the association between a face
signature and a known person.
[0104] The present invention, in yet another aspect thereof,
provides a novel method for generating face signatures based on
faces depicted in images. Further provided is a means for reducing
error rates in associating recognized face signatures with one or
more face signatures linked to a database.
[0105] The present invention, in a further aspect thereof, enables
the automatic selective distribution of images depicting faces. If
the faces detected in the images are associated with a person that
interfaces with the networked computer architecture herein
provided, the computer program herein provided may automatically
transmit the image to the person's computer for presentation to the
person. It should be noted that the terms "photo" and "image" are
used interchangeably herein.
[0106] The present invention, in a further still aspect thereof,
provides a novel advertising method that is operable with the
networked computer architecture herein provided.
Networked Computer Architecture
[0107] The present invention, in one aspect thereof, provides a
networked computer architecture enabling the automatic distribution
of images relating to a plurality of individuals operating computer
systems on the network. FIG. 1 illustrates an example
implementation of the networked computer architecture of the
present invention. A plurality of individuals may each connect to
the Internet (11) through computer terminals operable to access the
Internet (11). The Internet (11) connection enables the
transmission and reception of digital data from Internet connected
devices, each of which may be operable as provided below.
[0108] The present invention, in another aspect thereof, provides a
computer program operable to enable each of the individuals to
interface with the networked computer architecture herein provided.
FIG. 15 illustrates an example configuration of the system of the
present invention. The user (13) of the invention may register,
download, and install the computer program to its computer system
(15).
[0109] The computer program may, in one aspect thereof, allow the
user (13) to invite and establish relationships with other users of
the invention. The computer program may, in another aspect thereof,
request ads from a set of advertising web servers (23) that deliver
ads to the computer program for display to the user (13) on various
portions of a graphical user interface ("GUI"). The computer
program may, in yet another aspect thereof, transmit photos and
metadata related to those photos to other users or to third-party
websites the meta data can be stored in the EXIF or similar file
header or be embedded inside the jpg or similar image file format
in a manner similar to stenographic techniques (25) such as
FLICKR.TM. and FACEBOOK.TM.. These third-party websites (25) may
encourage the use of their websites by publishing application
programming interfaces (API's) to enable connection from client
software or other web-based applications to their service.
[0110] The components of the computer program enabling
implementation of the present invention may include: [0111] A
processing engine that may run as a background process on the
operating system. It may scan for new digital photos that the user
has uploaded or copied to the specified folders on the local and/or
a remote or cloud computer that are being monitored for photos, or
it may automatically detect new photos when removable media, such
as a memory card, is inserted into the computer. When a new photo
is detected the face detection, eye detection and face recognition
steps may be performed, as described more fully below. The results
may be stored in a database such as the database described more
fully below. It should be noted that the processing engine could be
executed on a remote computer or cloud service computer, such as
where the computer program of the present invention is provided as
a service, which may use the software-as-a-service model. [0112] A
GUI that may provide a user with the ability to navigate photos,
train the application by identifying known persons, edit and
correct the automatic results of the engine, create and modify
albums based on search criteria, add peer group members, and send
albums to third party websites, each such action described more
fully below. [0113] A database (such as a SQL database, for
example) that may be located on a user's computer or on a remote
computer or cloud computer, and may contain the results of the face
detection, eye detection and face recognition steps described
below. The database may also contain metadata for photos and people
as well as relationships between known persons and the associated
face images.
[0114] The GUI, the processing engine, and the database may be
provided on one or more devices. FIG. 21 illustrates one aspect of
the present invention wherein a remote web browser or mobile device
(83) is enabled to access a proxy server (81), providing a
connection to the functions of the present invention. The GUI may
be provided on a mobile device (83) such as a PDA or cell phone and
perform the face detection and recognition and and indexing and
transmit information back and forth to a remote engine running on a
website, server, or a user's desktop or laptop computer. In such an
implementation, the PDA or cell phone may be provided with a
facility for performing face recognition, browsing of images and a
facility for uploading images that are captured using a camera
incorporated on the device. Uploading may be performed in
accordance with the general uploading processes described more
fully below.
[0115] The steps performed by the user using the computer program
may include: [0116] Specifying the folder(s) to monitor for new
digital photos. [0117] Training the application by identifying the
names and metadata associated with faces found in the digital
photos. [0118] Correcting the errors made by the application; both
false positives and false negatives. [0119] Creating albums
(collections of photos) by specifying search criteria including
date ranges, Boolean combinations of known persons (via face
selection), EXIF tags, and general tags. Optionally, the user may
drag and drop individual photos or groups of photos to the album.
[0120] Once an album is created the user may then specify various
output options including: [0121] Third party websites such as
Flickr.TM. and Facebook.TM.. [0122] Slideshow formats such as
Microsoft.TM. Powerpoint.TM. files. [0123] Document formats such as
Adobe.TM. PDF.TM. files. [0124] Inviting other application users to
join in a peer group, specifying the options related to those
relationships in the peer group, and accepting invitations to join
a peer group for another user.
[0125] The networked computer architecture may also include one or
more servers to enable techniques described herein. For example,
the face detection and recognition method provided herein may be
enabled by the servers. The servers, which may be provided on one
or more server systems, may include server programs that enable the
functions of the servers, including one or more of the following:
[0126] A registration server having a database enabling association
of a list of email addresses, associated dates, and other
administrative data. The registration server may present an
interface such as a webpage to the user for collecting the
registration data and then writing this data to the database. The
user may then be given a means for installing the computer program
described above, such as by being provided with an URL for
downloading the client software. [0127] An ad matching server may
accept encrypted requests containing the demographic information
for the user. This server may also accept ads in a plurality of
formats (such as .JPG, .GIF, .SWF, etc.) from advertisers. For each
ad submitted to the application the target demographic for the ad
may also be recorded. The ad requests may be matched with the
inventory of ads based on the target demographic data. Pointers
(unique ID #'s) may be returned for matching ads to the requesting
client software. [0128] An ad delivery server may accept encrypted
requests containing pointers to ads. The application may find the
ad referenced by the pointer and return that ad to the requesting
client software.
Peer Groups
[0129] Referring again to FIG. 1, a first user (13) of the
invention may download the computer program from a website or be
provided with any other installation means for the installing the
computer program. The first user (13) may install the computer
program, which may enable the system of the present invention on
their laptop or desktop computer system (15) running an operating
system (such as the MICROSOFT.TM. WINDOWS.TM., APPLE.TM., or
LINUX.TM. operating system) or the program may be run within a
browser such as Microsoft.TM. Internet Explorer.TM., Mozilla.TM.
Firefox.TM., Google.TM. Chrome.TM., Apple.TM. Safari.TM. or mobile
browsers and as a result the program is executed within the browser
and on the back-end webservers.
[0130] The first user (13) may define friends (17) in a peer group
by specifying their email address to the invention. The computer
program may subsequently send an email invitation requesting that a
friend (17) also download or otherwise enable the installation of
the computer program. After installation, a corresponding computer
program for the friend (17) may present to the friend (17) a
pending request to join the peer group started by the first user
(13). The friend (17), who may now be a second user (17), may be
required to approve the connection to the peer group. Once
approved, the computer program run by the first user (13) and the
second user (17) can now exchange photos as well as metadata about
those photos and about known persons, in accordance with the image
sharing methods herein described.
[0131] The peer group may be expanded by the first user (13) or the
second user (17) by inviting more people (19, 21) to join the peer
group. The second user (17) may also create a new peer group that
the first user (13) is not part of, and expand that peer group
separately. There may be a "many to many" relationship between
people and peer groups. Thus the first user (13) can be a member of
multiple peer groups and the second user (17) can be a member of
multiple peer groups. This enables the easy sharing of photos with
other users based on peer group membership.
[0132] As described more fully below, the present invention, in one
aspect thereof, enables a known person list. Known persons may
optionally be added to one or more peer groups, as described
above.
[0133] Peer groups may enable sharing of photos, metadata about
photos, and known persons. The GUI may enable creation,
modification and deletion of peer groups by a user. The GUI may
also enable association of a face image or thumbnail of a known
person into an existing peer group, for example by enabling a user
to drag the face image or thumbnail over an area of the interface,
such as a field or icon, representing the peer group. A user could
assign a representative face image that is associated with their
name and that face image is shared and distributed to show up in
all peer group contact lists.
[0134] The computer program may generate an invitation each time a
friend is added to a peer group. Alternatively, the user may
manually add an invitee's metadata to the peer group if the invitee
is not part of the known person list. The invitation to an
individual may be sent to the invitee via email. For individuals
that have installed the computer program of the present invention
on their computer system, the email, once received, may prompt the
invitee to accept the invitation. Optionally, the individual will
be required to enter a code in the computer program to accept the
invitation.
[0135] If the friend has not yet installed the computer program or
is not registered in the cloud service implementation of the
present invention on their computer system, the email, once
received, may include a link register or to download or otherwise
enable installation and activation of the service and may provide
directions for installing the computer program on a computer
system. Following successful installation of the computer program
the new user may be presented with the invitation, and may accept
the invitation to join the peer group in accordance with the steps
described above.
[0136] Once the invitation is accepted by the invitee, the invitee
may be added to the peer group. The update may be disseminated over
the networked computer architecture to enable the corresponding
peer group information to be updated in the computer program of
each person associated with the peer group.
[0137] In accordance with the face detection technique described
below, the peer group may enable automatic selective dissemination
of information across the networked computer architecture. The
dissemination technique is also more fully described below.
Face Detection
[0138] The present invention, in one aspect thereof, provides a
novel method for generating face signatures based on faces depicted
in images. FIG. 3 illustrates a face recognition method in
accordance with the present invention, in one aspect thereof. A
user (13) may provide images to a computer system (15) operable to
enable the execution of a computer program. The computer program
may monitor file folders associated with the computer system (15)
via operating system notifications that may be generated when new
files are added or existing files are modified. When new images are
found (28) they may be queued (29) for processing.
[0139] Faces in the photos may be located (31) by using any one of
multiple techniques that may include: generating face coordinates,
which may define a bounding box around the face; determining eye
coordinates based on the face coordinates; and creating face
signatures (33) for the face based on the face and eye coordinates
and by using a face signature technique. The face signature
technique may be Principal Component Analysis (PCA), which is known
to those skilled in the art. The face signatures may be compares to
known face signatures (34) and the photos may be automatically and
selectively disseminated to other users (36). Further details of
these aspects of the invention are provided below.
[0140] FIG. 4 illustrates linking the results of a face detection,
eye detection and face recognition technique in a face database on
the storage device of the local computer system or alternatively
can be in a face database on the storage device of the cloud
computer system. The results may be the coordinates of the
associated object. In the case of face detection, the coordinates
may define the outline of the face (37) with top left, top right,
bottom left and bottom right pixel locations on the original photo
which is referred to as a bounding box. In the case of eye
detection, the coordinates may represent the pupil location (35) of
the left and right eyes. In the case of face recognition, the
result may be a face signature (42).
[0141] The graphical user interface (GUI) for the invention may
display the face (37) and eye (39) locations on each image (35). As
described more fully below, the present invention, in one aspect
thereof, provides a list of known persons. If the face signature
(42) corresponding to a detected face is associated with a person
listed in the known persons list, the GUI may indicate such an
association to the user using a graphic notation on or around the
image. Otherwise, the GUI may indicate that there is no such
association to the user using another graphical notation on or
around the image. In the example depicted by FIG. 4, the known
faces may be identified with check marks (41) and the unknown faces
with the symbol "X" (43).
[0142] Initially all faces may be unknown to the system until the
user "trains" the invention to recognize faces. The training method
may involve the user. The user, via the GUI of the invention, may
use the mouse or other input device to identify the face as
belonging to a specific person, by clicking anywhere on the visual
bounding box surrounding the face and dragging the face over top of
the person's name (or an icon representing the person).
Alternatively, the user may drag the icon representing that person
over top of the target face. In yet another alternative, the user
may click anywhere on the visual bounding box and select a function
for identifying a previously unknown face, which may enable the
user to enter data related to that person such as name, email
address and other details, which may collectively be referred to as
metadata corresponding to the person. This training step may be
performed once for each known person. The signature that was
created for the face may then enable comparison of all of the
unknown face signatures in the face database with the person
identified. Both the method for comparison and the method of face
detection, eye detection, and face recognition are described more
fully below.
[0143] The present invention, in a further aspect thereof,
facilitates an optimal training stage by ordering the unknown faces
such that the user can identify groups of detected faces that are
most likely associated with a single individual. For example, an
algorithm could be used to cluster similar faces together based on
face signatures. The similarity may be based on certain aspects of
their face signatures even when the faces are not already
associated with an individual in the face database. Thus a user can
identify a cluster of faces as belonging to a particular known
person and thereby optimally carry out the training method
described above.
[0144] The present invention, in a further aspect thereof,
facilitates an optimal training by more than one face signature
associated with a person thus improves accuracy by supporting
multiple poses of a person and addresses changes to the persons
face due to aging, glasses or changes to the face such as caused by
a beard or mustache.
Association of Faces with Known Persons
[0145] FIG. 16 illustrates an interface for enabling a user to
confirm the identity of a face appearing in an image. A face
signature in an image may be within a similarity threshold to a
face signature associated with a known person. In this case, an
association may be made between the detected face and the known
person. One method of comparing face signatures is described more
fully below.
[0146] If there is an association between the face and a known
person, a further association may be created in the database
between the face signature and the known person. Every previously
identified face for every known person may be compared with each
new face processed by the system. When viewing the faces related to
a specific known person, any suspected matches generated by the
invention may be displayed and the user may be asked to confirm
that the matches are correct.
[0147] Over time, as the number of identified faces increases, the
overall accuracy of matching new faces with the correct person may
increase since there will typically be many different views of a
person with each new face. In accordance with the method of
comparing face signatures provided herein, the number of false
positives therefore typically decreases over time.
[0148] FIG. 5 illustrates how peer groups can grow and evolve over
time as the list of known persons grows. The list of known persons
(101) grows as the user works with the invention, because the user
may continue to associate unknown faces with known persons.
Dissemination
[0149] The present invention, in another aspect thereof, provides a
computer program operable to enable each of the individuals to
interface with the networked computer architecture herein provided
for sharing images. FIG. 2 further illustrates the system of the
present invention. A user (13) may capture digital images and
periodically copy them from one or more image device storage
systems (27) to a computer system (15). The user (13) may configure
the computer program to monitor specific file folders on the
computer system (15) for new images, by inputting the names of the
file folders to the computer program using a GUI as described
above.
[0150] In accordance with the novel method for face recognition
provided by the present invention, the present invention, in one
aspect thereof, enables the automatic selective dissemination among
a peer group to users whose faces are depicted in images. This is
more fully described below.
Detection Optimizations
[0151] The present invention, in one aspect thereof, provides a
novel technique for optimizing face detections in accordance with
other aspects of the present invention. FIG. 6 illustrates
potential methods of correcting errors that may result from the
automatic face detection, eye detection, and face recognition
steps. The invention uses a novel way of involving the user through
the GUI to correct these inevitable errors.
[0152] FIG. 17 illustrates a means by which a user may delete false
positive face detections in an image. During the face detection and
eye detection phases, there may be false positive errors. These may
occur when the face detection technique determines that a face
exists even though there is actually no face on the original photo
image. To correct these errors the GUI may enable the user to
delete false positive errors by moving the face (103) (with the
face being identified by a bounding box on the image) over a
deletion area (which may be represented by a wastebasket or other
representative icon) using a mouse or other input device, by
pressing a keyboard's delete key while the face is highlighted, or
by selecting a menu option (105) corresponding to deletion of the
face.
[0153] FIG. 18 illustrates a means by which a user may reposition
detected eye coordinates corresponding to a face in an image for
the purpose of increasing accuracy of the detection algorithm.
During the face detection and eye detection phases, there may be
errors in eye location coordinates (107). The method of the present
invention may determine the eye pupil location and display the eye
coordinates (107) visually on the image, but the generated
coordinates may not be perfect because they may be an approximation
in some cases (for example, a face with sunglasses). The GUI may
allow the user to manually reposition the eye coordinates (107),
for example by moving the icons (109) representing the eye location
with a mouse or other input device. In this way, the accuracy and
performance of the invention can be increased as the eye
coordinates (107) are typically used to generate a face signature
for the face. A change in the eye coordinates (107) may therefore
generate a change in the face signature that may significantly
affect associating signatures with other with known faces. In
addition to the use of eye coordinate the method of the present
invention may employ an edge detection technique to align face
templates with the detected faces in an image. The alignment method
provides another method to confirm the face to reduce false
positives. This technique could be performed by aligning the
top/bottom and left/right sides of the face with the alignment
template and generating a numeric measure of confidence.
[0154] FIG. 22 illustrates deletion of a false positive face
detection error. During the face recognition phase, there may be
false positive errors related to incorrect association of a face
with a known person. A false positive error may result if the
invention matches a face signature to a known person and it is not
a correct relationship. The GUI may allow the user to drag the face
image (111) (thumbnail) over the face image (thumbnail) of the
correct known person (113). The invention may then modify the link
in the database to show the new relationship between the face and
the known person. The old relationship may also be deleted.
[0155] Another category of errors is false negatives. There may be
two situations categorized as false negative errors, which are
illustrated in FIG. 6: [0156] 1) Where the system does not detect a
face in an image when there actually is a face (47), the GUI may
allow the user to draw a bounding box around the face using a
mouse, or other input means, and then place both eye locations by
using icons representing the eye location. The system may then use
the manually entered information to generate a face signature and
perform the face recognition method provided herein. Alternatively,
the system may enable the user to manually associate a tag with the
face without involving the face detection or recognition process.
This manual tagging method may also be used to tag other objects
and animals in the images--such as pets--where the human face
detection method employed does not result in automatic detection of
said object or animal. [0157] 2) There may also be a false negative
error where the system detects a face but the face signature is not
matched with any known face signatures even though it is a face of
a known person. This may occur if the difference between the
signature for the face and any other face signatures for that
particular person are too dissimilar. In this case, the system may
not automatically detect the relationship and the face may remain
unknown. In this circumstance, the GUI may allow the user to drag
(49) the face image over the face image of the known person. By
doing this the system may link the face image to the known person
in the database. Wth this assistance from the user, the system now
has another face signature that will be used for future comparisons
with new and unknown face signatures. This may improve the accuracy
of the present invention. FIG. 19 further illustrates the process
of manually tagging a face in an image.
Automatic Selective Dissemination of Images
[0158] FIG. 7 illustrates a system and method for providing the
automatic selective dissemination of photos between users of the
invention. When the face of a known person that is a member of a
peer group is recognized within a photo (53), the photo (53) may be
queued to be transmitted across the Internet (11) in a secure
fashion to the peer group member. For example, a first user (13)
may upload to their computer system (15) a photo containing the
face of a known person that is a second user (17), who is also a
peer group member. In this case, when the system determines a face
match, the photo (53) may be queued for transmission. Prior to
transmission the photo (53) may be reduced to a smaller version and
metadata may be included in the digital photo file. The
corresponding reduction in size may optimize the use of bandwidth.
In a cloud implementation the photo may be physically transferred
or the file ownership may be expanded to include the peer group
member. The file may or may not be transferred or duplicated.
[0159] The next time the second user (17) accesses the computer
program on their computer system (51) it may receive a confirmation
request showing a reduced image of the original photo and the
associated metadata or the notification could go to the second user
via email also with thumbnails included in the body of the email or
as attachments to deliver the confirmation request. The second user
(17) may be prompted whether they would like a copy of the photo
(55) on its computer system (51). If the second user (17) responds
affirmatively then the system may copy the full image across the
Internet from the first user's computer system (15) to the second
user's computer system (55) along with the metadata for the photo
(55) and the known faces and signatures from the photo.
[0160] Another novel aspect of the present invention uses existing
social network service and web-based photo storage sites to share
photos with peer group members. The invention may transmit a single
photo or group of photos to a target web or cloudbased service. The
transmitted photos may already contain metadata from the invention
about the people that are in the photos. For example, the social
networking site FACEBOOK.TM. offers the facility to upload photos,
share photos, and to manually tag photos to denote which friends
are in the photos. The tagging process is manual and time
consuming. The present invention may automate such an upload
process and eliminate the need to manually tag the photos thereby
offering a significant benefit to the social networking user. The
present invention may be equally adaptable to other social network
service and web-based photo storage sites. As described above, a
user may also upload photos directly from a mobile device that
incorporates a camera across the Internet.
[0161] Yet another novel aspect of the present invention is that
the initial list of known persons can be loaded from the user's
social networking account to save further time in configuring the
system provided by the present invention.
[0162] FIG. 8 illustrates an example embodiment of the GUI that may
enable browsing of photos and the face database managed by the
computer program. In this example a user interface is shown with
optional banner advertising (57) that could be sold to advertisers.
A user may select a photo (123) from an album (125), and the GUI
may display thumbnails (121) corresponding to known persons (117)
found in the photo (123). Checkboxes (119) may also be used to
indicate the presence of the known persons (117) in the photo
(123).
[0163] The example depicted in FIG. 9 shows face images for known
persons plus the checkboxes for applying Boolean searching such as
AND, OR, and NOT selections associated with names of known persons
or metadata related to images. A novel feature of the invention is
the ability to select photos in a visual manner by allowing the
user to click on a thumbnail view of the faces of known persons
(59), and applying Boolean operations (61) for each face enabled by
checkboxes. This aspect of the GUI enables the creation of an album
by combining various search criteria and filters that are applied
against the total photo and face database. In a further embodiment
of this invention, the user could assign an image (real,
hand-generated or computer generated) to a specific person and that
replacement image is overlaid over the portion of every image where
that specific person is found.
[0164] Search criteria provided by the computer program may
include: [0165] Folder selection (65), indicating the folder
location or file name of photo images on the computer storage
device; [0166] Known persons (67), providing the selection of
Boolean operations (AND, OR, or NOT) associated with the faces of
the known persons; [0167] Date range (69), providing the selection
of a range of dates corresponding to the photo images to be
included in the specific album; [0168] EXIF data, providing a
selection means based on standard photo related information usually
appended to the photo by the camera during the photo taking
process.
[0169] An alternative method of selecting photos for an album may
provide the user the ability to drag and drop individual photos or
groups of photos over top of the name of the album (63), or to
otherwise associate the photos with the album (63), using the
GUI.
[0170] An album (63) may be a group of photos that are saved as a
logical entity under the name of the album. The user may specify
via the GUI that it wants to send the album to various target file
types or locations (71), including a slide show, MICROSOFT.TM.
POWERPOINT.TM. or other presentation computer programs, ADOBE.TM.
PDF.TM. or other document file, a web-based sharing site such as
FLICKR.TM. or FACEBOOK.TM., or a third party printing service.
Advertising Method
[0171] The present invention, in an optional, non-limiting aspect
thereof, provides a novel advertising method that is operable with
the networked computer architecture herein provided.
[0172] FIG. 10 illustrates an optional advertising display
capability provided by the GUI. This is a novel feature of the
present invention providing a secure method for simultaneously
targeting advertising based on user demographics and maintaining
user privacy. The GUI may prompt new users (13) for demographic
information that may at a minimum include gender, age and location
data. This information may be stored locally on the computer system
(15) running the computer program. A request may periodically be
sent to a web-based server (73) to return a list of ad pointers.
The request may contain an encrypted transmission of the
demographic data for the user. The request may also be signed using
a certificate issued by a registration server (77). This latter
step may verify the authenticity of the request. The web-based
server (73) may conduct a process of matching ads, which are
associated with target demographic information, to the requesting
user (13) based on their specific demographic information. A list
of pointers (i.e. references to ads that exist on different ad
servers) may be returned to the requesting user's computer system
(15) and passed to the computer program. The computer program may
then issue another request to a different web-based server (75) to
download the ads referenced by the pointers. The web-based server
(73) may then discard the demographic data to protect the privacy
of the individual user.
[0173] By separating the web-based servers (73, 77) that perform
the ad matching process from the servers (75) that actually deliver
the ads, and by not storing the personal demographic data of the
user on either of the web-based servers (73, 77), the personal
information about the user is significantly more secure than it
otherwise would be. The ad delivery servers (75) may store
information about ads served for billing purposes but there may be
no personal information included in that data. This is a novel
implementation for serving ads to any web browser or software
program in a secure fashion using demographic data.
Further Implementations
[0174] In another exemplary non-limiting optional aspect of the
present invention, another capability of the present invention may
enable a computer program to receive digital face images or
signatures from the central registration server. For example, an
organization seeking to find an individual (such as a missing child
or a wanted criminal) may post the individual's face data. Those
users that have opted to share their face database may download the
data enabling an automatic comparison of the face data with their
face database. The organization may be alerted if a match is found
between the target individual and a known person for a specific
user. This could enable the organization to determine a recent or
current location of the individual. It could also enable the
organization to determine the name of an individual, since the
individual's name may be listed in one or more of the user's known
persons list.
[0175] Yet a further embodiment provided by the present invention
may enable an individual to find other persons with similar facial
features as themselves. Such an application may be useful for a
person to find their twin, for example. In this embodiment, a user
may submit a photo including an image of their face, from which the
present invention may generate a face signature. The face signature
may then be compared to other individuals' face signatures that
have been previously uploaded. Based on a predetermined similarity
threshold, one or more similar faces may be reported to one or all
of the individuals whose faces match. A system in accordance with
this embodiment may provide a means for a user to approve contact
with others. Matched individuals could choose to send a message to
one another if they have specified their approval for such contact.
A similar embodiment could be deployed as part of a dating service
to match people based on looks.
Face Detection, Eye Detection and Face Recognition
[0176] The present invention, in yet another aspect thereof,
provides a novel method for generating face signatures based on
faces depicted in images. The face signatures may be generated by
using a technique that includes the steps of face detection, eye
detection, and face recognition.
Face Detection
[0177] The present invention, in one aspect thereof, provides a
method for utilizing a texture-based face detection algorithm as a
base method for face detection. One example of a texture-based face
detection algorithm is the open source library of routines known as
OPENCV.TM..
[0178] The texture-based face detection algorithm may have a low
true-positive rate for specific facial poses, for example rolled
frontal faces. This may be due to the texture pattern being trained
on frontal face images which differ from the varied facial poses
found in normal digital photos. In reality, having a mismatch
between the texture pattern used to train the face detection
algorithm and the type of poses in the target photos on which the
algorithm is applied would result in a higher percentage of errors.
Two well-known challenges presently faced in face detection include
decreasing false-negative errors in the case of rolled frontal
faces and reducing false-positive errors while not increasing false
negative errors.
[0179] The present invention enhances the texture-based face
detection by applying novel techniques involving three steps to
improve the accuracy of the face detection process. In the first
step a novel application of the known skin color ratio test on a
texture-based face detector is provided to improve the accuracy of
the detector. In the second step, a novel method for reducing
false-positive face objects is provided by reducing the size of
detected face object regions to a predefined size. In the third
step, a novel face orientation compensation method is provided.
[0180] FIG. 11 illustrates the texture-based face detection method
of the present invention, in one aspect thereof.
[0181] In one particular implementation of the texture-based face
detection method, in the first step, the texture-based face
detector (131) may initially be set for a high target true positive
rate which has a corresponding high false positive rate. The
texture-based face detector may be run with the entire photo image
as input. The results from this run may give a list of potential
face objects in the photo. Any candidate face regions detected may
be recorded in a database. For a color photo, a skin color
detection test (133) may be performed on the potential face objects
to reduce the false positive rate. This skin color test may compare
the ratio of the face object area containing skin color to the
total area of the object. If the ratio does not exceed a
pre-determined threshold then the potential face object may be
skipped (135).
[0182] In the second step, detected face object regions that result
from the first step may be reduced to a predefined size (such as 44
by 44 pixels). On these reduced regions, the texture-based face
detector may be run again (137). The goal of this step is to reduce
false-positive face objects. By running on a small size (such as 44
by 44 pixels) input region, false positive errors from incorrect
texture patterns from non-faces may be reduced while true positive
texture patterns may be preserved. This may result in a reduction
in the false positive rate of face detection while preserving the
true-positive rate. Face objects that are deemed to be faces in the
second step may be accepted (143) as true faces. Those that do not
pass this second step may be passed to a third step.
[0183] In the third step, a face orientation compensation method is
provided. In the second step described above, in which face
detection is performed on smaller regions of the image, the true
positive rate may be reduced in the case of rolled faces. The
texture pattern in rolled faces may be deemed to be a non-face by
the face detector due to the size reduction applied in the second
step. To preserve the true positive rate in rolled face cases, face
orientation compensation (139) may be performed together with face
detection. In this method, the local image region is rotated with
an incremental angle and each angle instance may be run through the
face detector. One example implementation would rotate the image 2
degrees starting at -20 degrees from the vertical axis and
repeating the rotation through +20 degrees from the vertical axis.
If rotated face regions are recognized and exist in instances with
consecutive incremental angles then the local image region may be
determined (141) to be a true face.
[0184] These modifications to a texture-based face detection
algorithm may significantly reduce the false positive error
rate.
Eye Detection
[0185] FIG. 12 illustrates a method for eye detection, in one
aspect of the present invention. The eye detection method applies
novel techniques to improve the accuracy for detecting the pupil
locations in detected face regions. The first step may be to reduce
the overall face region to a smaller region (an "eyemap") (145)
that would likely contain the actual pupil/eye locations. A formula
may be used to crop the eyemap region from the face region. For
example, the formula used to crop the eyemap region may be to
remove 0.23w on the left, 0.23w on the right, 0.55h on the bottom
and 0.30h on the top; where w is the width of the face region and h
is the height of the face region. FIG. 14A illustrates a particular
embodiment of the method of cropping the eyemap region from the
face region.
[0186] Furthermore, if the face was detected in the third step of
the face detection method on a specific angle instance, then the
angle of rotation may be applied (147) to the eyemap region to
enable a more accurate selection of the eye locations.
[0187] FIG. 14B illustrates that the formula used to crop the
eyemap region may be altered to ensure that the eyemap region is
large enough to ensure satisfactory pupil detection results.
[0188] Once the eyemap region has been cropped, the eyemap region
may then be re-sized to a fixed size (such as 80.times.80 pixels)
(149) so that a fixed template color variation method may be
applied to determine the candidate eye locations. This method may
be based on the darker intensity of the pupil relative to the
surrounding area of the eyemap region.
[0189] The middle of the color intensity image may be set to
zero/black (151) in the intensity map to remove potential pupil
candidates that typically result from light reflecting off lenses
and the frames of glasses. FIG. 14C illustrates this process.
[0190] The color variation image may be obtained from the color
intensity image and may then be passed through a threshold filter
so that only a small percentage of the eye region is white (for
example, a 2% threshold may be applied) (153). Next, a "best fit"
method may be applied to choose the eye locations with the highest
intensity of color variation (left and right side). The best
candidate pupil location coordinates may be passed along (155) to
the face recognition method of the present invention.
Face Recognition
[0191] FIG. 13 illustrates the face recognition method, in
accordance with aspects of the present invention. Face recognition
may generally be performed through pre-processing (157), projection
(159), distance calculation (163) and aggregation (167). Principal
Component Analysis (PCA) may be employed by the face recognition
method. PCA is a known method, used abundantly in all forms of
analysis because it is a simple, non-parametric method of
extracting relevant information from confusing data sets.
[0192] The present invention may employ PCA in a novel way that
overcomes its traditional issues with respect to high sensitivity
to subject lighting and pose, given that personal photos may have
little variation in pose since most subjects may tend to look
directly at a camera. The present invention may take advantage of
the fact that sets of photos depicting particular persons may be
taken over a variety of pose and lighting conditions. The present
invention provides a method of aggregating the comparison between
the target unknown face and this plurality of faces associated with
each known person.
[0193] Pre-processing (157) may first be applied to the image. The
face region and eye coordinates provided as input to the face
recognition phase may be used to normalize the face region. The
normalization process may include translating, rotating and scaling
the face region to a predetermined template size. The normalization
process may use the eye coordinates as reference points such that
the face region image is adjusted to place the eye coordinates on
specific image pixels. A standard fixed masking process,
potentially limiting the face region to the area inside an ellipse,
may then be applied to the image to filter out non-face portions. A
flattening process, involving running a two dimensional linear
regression on the intensity of the pixels in the face region, may
be used to ensure pixel intensity is spatially uniform across the
image. Finally, a histogram image equalization (an image processing
method known to those skilled in the art whereby the contrast of an
image is adjusted using the image's histogram) may be performed in
the greyscale domain.
[0194] Projection (159) may then be applied to the image. The
resulting pixels of the normalized face region may be passed
through a PCA-based formula to create a PCA vector that is used as
the face signature by the invention. The PCA vector may comprise a
projection image resulting from principal components extracted from
a large set of generic images.
[0195] The face signature created from this method may be an array
(from the PCA vector) of real numbers of a given dimensionality.
Although the exact dimensionality of the vector space may be
determined adaptively with its maximum value set to the value
capturing, for example, 95% of the input image energy, the default
value used may be a dimensionality in the range of 50 to 100.
[0196] Finally, looping (161) may be applied to match the unknown
face with a known person. Each face signature (represented as an
array of numbers) may be mathematically compared to any other face
signature using linear or non-linear classification logic to
determine a distance value (163). For example, two signatures may
be compared by computing a normalized inner product distance.
[0197] To compare a face to all of the faces associated with a
known person, all of the individual one-to-one comparisons may be
made, and then either all of the results may be used in the next
step or a set of best matches as determined by comparison to some
threshold (165) may be used. The threshold chosen may be chosen
such that on average half of the distances obtained when comparing
a face to a non-matching person are kept.
[0198] Some form of aggregation may be used to combine the set of
distance values obtained in the previous step to determine the
distance between the face and a known person. This aggregation may
be the computation of the geometric mean of the distance values
(169). The geometric mean may be an averaging technique similar to
an arithmetic mean, but it may be computed by multiplying the N
numbers to be averaged and then taking the Nth root of the product
as the desired average. The closest match between the face and each
known person may be found by computing this aggregate distance
(167) between the face and each known person in the database and
choosing the minimum distance.
[0199] Finally, the closest match distance may be compared (171)
against a static or dynamically determined threshold to reduce the
rate of false positive matches. If a dynamically chosen threshold
is used, this threshold may be determined by first assuming the
aggregate distance values obtained when comparing a face to a
non-matching person having N associated faces are normally
distributed (for each possible value of N), and then using the
inverse cumulative normal distribution function to compute a
threshold which ensures that, on average, a fixed maximum number of
or fixed ratio of the unknown faces are falsely matched to a known
person. This threshold may vary from person to person as the number
of faces, N, associated with each person changes. The advantage of
this dynamic threshold calculation includes that the fixed maximum
number (or ratio) may be kept as small as possible to limit false
positive matches while maintaining an acceptable level of true
positive matches for the user.
[0200] FIG. 13a also illustrates the face recognition method, in
accordance with aspects of the present invention. The face
recognition method shown may normalize a face based on eye
coordinates. Each face may be defined by a bounding box determined
by the face recognition method. The pixels based within the
bounding box may be extracted to create an image of the face. The
face image may be recorded to a database and associated with the
respective source image from where the face image was extracted.
The a PCA vector for the normalized face may then be calculated and
stored in a database. The PCA vector may be compared against the
known faces in the database. If a match is found, a face pointer
may be stored in memory, such as temporary memory. If after looping
through all of the known faces in the databases a match is not
found, then the face recognition method determines whether a face
was found in the search. If no face was found, then the face
recognition ends. If a face was found, then the face recognition
method may determine the best match in memory if more than one face
match is found. The face recognition method determines whether the
best match exceeds a threshold for closeness, as described above.
If the threshold for closeness is exceeded, then the best match is
written to the database. If the threshold for closeness is not
exceeded, then the face recognition ends.
[0201] The present invention, in a further aspect thereof,
facilitates an optimal training by more than one face signature
associated with a person thus improves accuracy. The invention may
allow the user to select the representative subset of known faces
for a given individual and thus over-ride the subset chosen by the
invention. That subset would then be used by the recognition engine
to determine suggested matches and ignore other known faces for
that person. Examples where this would be beneficial would be where
there multiple poses of a person. It also addresses changes to the
persons face due to aging, glasses or changes to the face such as
caused by a beard or mustache. By selecting more recent photos as
the representative subset, the probability of successful automatic
selection for future photos would increase. As the number of face
signatures grows that are linked to known people in the local face
database, the accuracy of the invention may increase in detecting
known people automatically in future photos that are processed.
This is a novel feature of this system.
[0202] The invention may learn a face by determining the closest
match for a previously identified face from the group of known
persons. An advantage of the present invention is that as the
number of face signatures linked to known persons in the local face
database grows, the accuracy of the invention may increase in
detecting known persons automatically in future photos that are
processed.
Video Scanning
[0203] FIG. 20 illustrates a video scanning method whereby frames
of video are extracted and face detection is performed on these
frames. A number N may be set (where N is adjustable) such that
video may be scanned every N frames (181) of the video as
individual photo images (183) where the previously mentioned
techniques (185) would be applied to detect and recognize faces and
known persons. The video could then be disseminated in accordance
with the techniques provided herein.
[0204] An optional, non-limiting embodiment provided by the present
invention enables an individual to find other persons with similar
facial features as themselves. Such an application may be useful
for a person to find their twin, for example. In this embodiment, a
user may submit a photo including an image of their face, from
which the present invention may generate a face signature. The face
signature may then be compared to other individuals' face
signatures that have been previously uploaded. Based on a
predetermined similarity threshold, one or more similar faces may
be reported to one or all of the individuals whose faces match. A
system in accordance with this embodiment may provide a means for a
user to approve contact with others. Matched individuals could
choose to send a message to one another if they have specified
their approval for such contact. A similar embodiment could be
deployed as part of a dating service to match people based on
looks.
Face and Portrait Extraction and Creation
[0205] FIG. 23 illustrates the creation of thumbnails or portraits
in one aspect of the present invention. Where faces are located in
an image 23a one aspect of the present invention captures the XY
coordinates of the location of the faces shown in image 23a
providing a method to capture a face and adjust the image of the
face both in terms of size and rotation to create a portrait, which
may either be full size or a thumbnail. As illustrated in FIG. 23
the coordinates may define the outline of the face with top left,
top right, bottom left and bottom right pixel locations on the
original photo which is referred to as a bounding box. The XY
coordinates are recorded in the database as illustrated in FIG.
11.
[0206] As illustrated in FIG. 23, the faces are located as shown in
image 23a and a corresponding bounding box for each located face
determined by the XY coordinates is established and recorded in the
database as illustrated in FIG. 11. As shown in images 23b and 23c
of FIG. 23, the XY coordinates to make the bounding box may be
adjustable to make subsequent creation of the bounding box, the
area around the face, larger or smaller. Using eye location and
identification as depicted in FIG. 18 by reference numbers 107 and
109, the face may be rotated to make the face vertical as shown in
image 23d of FIG. 23.
[0207] The record of the portrait/thumbnail image 23d may be
recorded in the database as illustrated in FIG. 13 for future
use.
[0208] In accordance with at least one exemplary implementation of
the present invention, at least one computer terminal, server, or
other computing device may detect at least one face in at least one
digital image. The computer may determine and store area
co-ordinates of a location of the at least one detected face in the
at least one digital image. The computer may apply at least one
transformation to the at least one detected face to create at least
one portrait of the at least one detected face. The computer may
rotate the at least one portrait at least until the at least one
portrait is shown in a vertical orientation and a pair of eyes of
the at least one face shown in the at least one portrait are
positioned on a horizontal plane. The computer may then store the
rotated at least one portrait. Each detected face may correspond to
a person. A copy may be made of or extracted from the portion of
the digital image comprising the detected face prior to the
application of the at least one transformation.
[0209] An identification of a person may be associated with each
stored face portrait in a database stored at or linked to one or
more computers.
[0210] Where a digital image shows more than one person, the one or
more computers may associate relationships between identified
persons based at least partly on respective identified persons
being included in the digital image.
Advertising and Product Personalization
[0211] FIG. 24 illustrates merging portraits or thumbnails adjusted
in terms of size and rotation and embedding the same into an
advertising message or product, or any other type of marketing
material or image. The thumbnail or portrait 24a, also illustrated
in FIG. 23 as image 23d may be adjusted in size to match the size
requirements of an advertisement 24b. For example, the computer may
apply at least one additional transformation to the at least one
stored portrait for matching a portrait size requirement of the
marketing image.
[0212] FIG. 32 illustrates the workflow for the creation of the
advertisement. As illustrated in FIG. 32 when an advertisement is
to be personalized one aspect of the present invention matches an
advertisement retrieved from an advertisement server database 32b
to a user which is retrieved from the user demographic and portrait
database 32a. The portrait retrieved from the user demographic and
portrait database 32a may be resized at 32c to match size
requirements of the advertisement retrieved from the advertisement
server database 32b. The user portrait can be merged with or
overlaid on top of the advertisement to display a personalized
advertising message. In addition to the portrait, the user's name
or other information pertinent to the advertisement message may
also be merged with or overlaid on top of the advertisement to
display a personalized advertising message as demonstrated by the
advertisement 24b.
[0213] FIG. 33 illustrates the workflow for the personalization of
a product. As illustrated in FIG. 33 when a product is to be
personalized one aspect of the present invention matches a product
retrieved from product database 33b to a user which is retrieved
from the user demographic and portrait database 33a. The portrait
retrieved from the user demographic and portrait database 33a is
resized at 33c to match the size requirements of the product
retrieved from the product server database 33b. The user portrait
can be merged with product to display the personalized product 24c
shown in FIG. 24. In addition to the portrait, the user's name or
other information pertinent to the product may also be merged with
the product to display a personalized message as demonstrated by
the resulting personalized product 24c.
Face Substitution
[0214] FIG. 25 illustrates using a selected image to overlay on a
digital image photo to cover the face of a subject, also known as
face substitution. An application may be to hide negative
memories.
[0215] FIG. 34 illustrates a workflow for face substitution. When a
user wants to hide negative memories one aspect of the present
invention may matche faces in the face database 34a to be hidden in
the photos from the photo database 34d with an image that is
selected or provided by a user which is stored in the negative
memory image database 34b. The databases 34a, 34b, and 34d may be
found on one computer server in a single database, in separate
databases on the same computer, or on databases stored on or across
multiple computers. The image retrieved from the negative memory
image database 34b is resized 34c to match the size requirements of
the faces to be hidden in the photos 25a retrieved from the photo
database 34d. The image is merged with photo 25a such that when the
photo is displayed the image hides the face of the negative memory
as demonstrated by the resulting hidden face photo 25b. In a
non-limiting aspect of the present invention, one or more computers
may receive a selection of at least one face to be suppressed,
optionally as any form of user input. The one or more computers may
then mask the area co-ordinates corresponding to the location of
the at least one face to be suppressed in the at least one digital
image.
[0216] Optionally, the masking may comprise overlaying a selected
image over the area co-ordinate corresponding to the location of
the at least one face to be suppressed. Optionally, the one or more
computers may adjust the selected image to correspond to size of
the area co-ordinates to be masked. Optionally, the one or more
computers may adjust edges of the overlayed image to blend with
image characteristics of the at least one digital image. For
example, the edges of the overlayed image may be blurred or the
opacity of the edges may be modified. Optionally, the image
characteristics to be blended may include one or more of color,
intensity, brightness, and texture. Optionally, the selection of
the at least one face to be suppressed may include a command to
suppress a particular individual from being displayed in any
digital image, the method comprising identifying any digital image
comprising a face corresponding to the particular individual
(identified person) to be suppressed and flagging the identified
digital images to mask the area co-ordinates corresponding to the
location of the at least one face to be suppressed. This may be
accomplished by directly modifying the respective digital image(s)
or updating associated metadata or other information or data to
cause a display of the respective digital image(s) in a
non-destructive manner such that the original source digital image
is not permanently modified in the database. Accordingly, as mask
may be applied to the digital image permanently, or the masking may
involve modifying metadata of the digital image to cause the
digital image to be masked when displayed.
[0217] FIG. 26 illustrates the display of faces captured from
photos in historical order, by date associated with the respective
photos, optionally in metadata stored with the photo or elsewhere.
The respective date may be the date the photo was created, copied
from a camera, transferred to a computer system of the present
invention, or any other date associated with the photo. Wth such
ordering the selected faces can be merged to create a single
composite image as illustrated by 26b. Such faces can also be
automatically fed into a morphing application such that the aging
of a person can be animated.
[0218] Optionally, date data may be associated with the digital
image. Stored portraits corresponding to a common person may be
displayed in accordance with the respective associated date data.
Optionally, an animated representation may be generated of an
evolution of the stored portraits corresponding to the common
person over time.
[0219] FIG. 27 illustrates the display of faces/portraits captured
from photos and embedding the faces or portraits into games,
exercises, or other interactive applications.
[0220] FIG. 35 illustrates a workflow for creating customized or
personalized games and exercises using user portraits and photos,
in accordance with aspects of the present invention. A user may
select a game or exercise to be played from the game database 35c.
The invention selects portraits from the portrait database 35a or
photo database 35b for display. The portraits or photos are resized
at 35d to match the size requirements of the game to be played. The
resized portraits or photos are merged with the game's user
interface for the user to play. Databases 35a, 35b, and 35c may be
found on one computer server in a single database, in separate
databases on the same computer, or on databases stored on or across
multiple computers.
[0221] Optionally, the one or more computers may populate an
interactive computer game with the stored portraits and
relationships. The interactive computer game comprises a photo
reminiscence therapy game.
Indexing Systems Automated Using Face Recognition
[0222] FIG. 28 illustrates the creation and use of a family tree to
index photos and FIG. 36 illustrates a corresponding workflow.
Portraits may be displayed from the portrait database of identified
people. The user may drag and drop each portrait on to the family
tree at 36b using the relationship template 28b as illustrated in
FIG. 28. In a non-limiting example, each user of the system of the
present invention may have six basic relationships of mother,
father, sibling, spouse, daughter and son, as shown in template
28b. As the user drags portraits on to the family tree the family
tree grows to show the new nodes. Optionally new blank nodes are
added for the common relationships to the selected node. As more
persons are added the family tree expands as users are added and
relationships are defined. When the user has dragged all portraits
the family tree is completed by eliminating extraneous relationship
or persons such as friends and colleagues. Users will likely have
friend and colleague relationships that are pertinent to their
personal social map but are identified in a modified network map
using a similar drag and drop method.
[0223] The family tree 28a which is created may be used as an index
to the user's photos at 36d. When a user selects or clicks on a
photo a database query will be executed at 36e to display the
photos in which the selected person appears.
[0224] Instead of a family tree 28a, other types of organizational
structures representing relationships between persons may be
presented in a chart format for populating by dragging portraits
thereto, or otherwise selecting portraits. For example, a corporate
organization chart may be created by providing a blank, or
incomplete corporate organization chart. The user may then be
presented with portraits from the database for placement on the
chart.
[0225] In accordance with a non-limiting aspect of the present
invention, at least one computer may include or interface with at
least one database that stores a plurality of portrait images. Each
portrait image may be associated with an identified person shown in
the respective portrait. The identification of the person may be
stored in the database, another database, or in metadata associated
with the respective portrait image. The at least one computer may
display the respective portrait of at least one identified person
associated with a user. The at least one computer may display a
visual representation of at least one personal relationship to the
user. The at least one computer may assign at least one of the
displayed portraits to at least one of the displayed personal
relationships, in accordance with a received user input. The at
least one computer may store the personal relationship assignments
in the database. The visual representation may include a
representation of a tree organizational structure with a plurality
of tree nodes, such as for a family tree, where each tree node
corresponds to one of the at least one personal relationships. The
at least one computer may spawn a tree node for the visual
representation corresponding to at least one additional personal
relationship in accordance with a user input adding at least one of
the displayed portraits to the visual representation. For example,
the family tree structure may grow as additional portraits are
added to the tree. The user may then specify a relationship for the
newly added portrait, or a default relationship may be assigned, or
the at least one computer may attempt to determine an appropriate
relationship for the new relationship based at least partly on data
found on a social network system to which the user is a member. The
database of portraits, or another database accessible to the at
least one computer, may include a plurality of digital images, and
the at least one computer may index at least a subset of the
plurality of digital images in accordance with the visual
representation. Accordingly, the family tree structure may be used
to link to other digital images featuring members of the family
tree. In particular clicking on one member of the family tree may
link to one or more digital images showing at least that member of
the family tree.
[0226] FIG. 29 illustrates the use of group photos to be an index
for photos in the photo database, and FIG. 37 illustrates a
corresponding workflow. A user may select a photo or photos from
the photo database 37a. The photo selected is recorded as a photo
for indexing photos and becomes an index in one aspect of the
invention. When a user selects or clicks on a face in the photo the
one or more computers may search database 37a or another database
for more digital images including the identified person
corresponding to the face in the photo the user has selected.
[0227] In accordance with a non-limiting aspect of the present
invention, at least one computer may include or interface with a
database of portraits and digital images, each portrait associated
with an identified person shown in the respective portrait. The at
least one computer may display at least one of the digital images
and cross-reference the displayed at least one digital image with
the database of portraits to create a list of at least one
identified person shown in the at least one digital image. The at
least one computer may index at least a subset of the digital
images in accordance with the list of at least one identified
person. In accordance with a user input selecting at least one of
the identified persons from the displayed at least one digital
image, the at least one computer may display at least one of the
respectively indexed digital images. Optionally, the indexing may
include displaying visual representations of the indexed digital
images organized by identified person. Solicited and automated
search for missing persons
[0228] In yet another aspect of the present invention, the present
invention may enable a computer program to receive digital face
images or signatures from a third party server, such as a central
person search server. FIG. 30 illustrates displaying the delivery
of face signatures of missing children and wanted criminals that a
user could elect to use for searching the user's photo database(s)
for potential matches. A corresponding workflow is shown in FIG.
38. For example, an organization seeking to find an individual
(such as a missing child or a wanted criminal) may post the
individual's face data shown in images 30a and 30b. The face data
may originate from a person search server 38a. Those users that
have opted to share their face database 38b may download the data
enabling an automatic comparison of the face data with their face
database 38b. The results of the comparison may be displayed to the
user at 38c for validation. The user may notify the organization at
38d if a match is found between the target individual and a known
person for a specific user. This could enable the organization to
determine a recent or current location of the individual. It could
also enable the organization to determine the name of an
individual, since the individual's name may be listed in one or
more of the user's known persons list.
[0229] Yet a further embodiment of the present invention, described
in reference to FIG. 31 and corresponding workflow FIG. 39, may
enable an individual to find other persons with similar facial
features as themselves. Such an application may be useful for a
person to find their twin, for example. In this embodiment, a user
may elect to submit a photo including an image of their face, from
which the present invention may generate a face signature stored in
face signature database 39a. The face signature may then be
compared to other face signatures that have been previously
uploaded to database 39a or to look alike database 39b. Based on a
predetermined similarity threshold, one or more similar faces may
be reported to one or all of the individuals whose faces match at
39c. A system in accordance with this embodiment may provide a
means for a user to approve contact with others at 39d. Matched
individuals could choose to send a message to one another if they
have specified their approval for such contact. Another variation
on this invention would be to look for people with similar facial
features taken separately from the entire face--such as mouth,
nose, and eyes.
[0230] In accordance with a non-limiting aspect of the present
invention, at least one computer may include or interface with a
database of portraits, each portrait associated with an identified
person shown in the respective portrait. The at least one computer
may receive at least one unidentified portrait and compare a face
signature of the at least one unidentified portrait against face
signatures of portraits of identified persons known to the user. In
accordance with a positive result of the comparing, the at least
one computer may prompt the user for confirmation of the positive
result. In accordance with the confirmation, the at least one
computer may associate the at least one unidentified portrait with
the at least one identified person confirmed by the user and store
the at least one unidentified portrait in the database.
[0231] Optionally, the unidentified portrait shows a missing
person. Optionally, the receiving may include several steps. First,
the digital image may be received. The at least one computer may
then detect at least one face in the received digital image. The at
least one computer may determine and store area co-ordinates of a
location of the at least one detected face in the received digital
image. The at least one computer may apply at least one
transformation to the at least one detected face to create the at
least one unidentified portrait of the at least one detected face.
The at least one computer may rotate the at least one unidentified
portrait at least until the at least one unidentified portrait is
shown in a vertical orientation and a pair of eyes of the at least
one face shown in the at least one unidentified portrait are
positioned on a horizontal plane.
Visualizing Relationships from Photo Metadata
[0232] When a set of digital photos contains name tags that
identify the people represented in those photos then there is
potential to "mine" that information and generate potentially
interesting, entertaining, and useful techniques for displaying
relationships between people that have been tagged in those photos.
How the name tags are made available to the computer system is not
limiting to the concept of using that data to display interesting
graphs and charts. One approach to generating the name tags quickly
is to use face detection and recognition technology. This
technology speeds up the tagging process by automating most of the
manual steps. The name tag data could simply exist in the photo
metadata, such as in the Adobe XMP format, or it could exist in a
SQL or other database.
[0233] FIG. 46 depicts a graph generated in accordance with an
aspect of the present invention. The relationships between people
appearing in photos are shown by interconnected lines between
people. Starting with a central, or selected person (in this
example: Ray), the invention shows a node that represents the
number of photos in which Ray appears. The node may be sized
relatively according to the number of photos. Any nodes attached
directly to the central "Ray" node may identify people that appear
in photos with Ray. So for example, April appears in 100 photos
with Ray, and "Friend A" appears in 135 photos with Ray. Then the
next layer of the diagram shows people that don't appear with Ray,
but appear with the people connected directly with the Ray node.
So, Sandy and Lisa appear together with April in photos. This
method is used to graph all relationships moving outward from a
central person. Each node may show a frequency of appearance
together of the person named at the node and the person named at
the immediately preceding node.
[0234] This graph in FIG. 46 could be used as an index to the
photos. By clicking on any node in the graph, the corresponding
photos represented by that node could be displayed in slideshow or
thumbnail list format. The at least one computer could query the
one or more photo databases in advance of receiving a click on any
of the nodes in order to be prepared to present the corresponding
photos, or the at least one computer could perform any such query
or queries after having received the click user input.
[0235] In accordance with a non-limiting aspect of the present
invention, at least one computer may include or interface with a
database of digital images and respective metadata identifying a
name of at least one identified person shown in the respective
digital image. The at least one computer may select one of the
identified persons from the database and determine a count of a
total number of digital images where the identified person appears.
For each identified person shown in at least one of the digital
images with the selected identified person, the at least one
computer may determine a count of a total number of digital images
where the respective identified person appears with the selected
identified person and display a visual representation, such as a
graph or chart as shown in FIG. 46. The visual representation could
include a first node representing the selected identified person
(e.g. "Ray" in FIG. 46) and the respective count of the total
number of digital images where the identified person appears. For
each identified person shown in at least one of the digital images
with the selected identified person, a second node may be presented
representing the respective identified person shown in at least one
of the digital images with the selected identified person. Each
respective node may further include a visual representation of the
respective count of the total number of digital images where the
respective identified person appears with the selected identified
person. Each second node may be linked to the first node,
optionally in the form of a visible line or by other means of
displaying a visible link.
[0236] Optionally, for each identified person displayed with a
respective second node (a "second person"), for each identified
person shown in at least one of the digital images with the
respective second person, the at least one computer may determine a
count of a total number of digital images where the respective
identified person appears with the respective second person. In
this case, the visual representation may include, for each
identified person shown in at least one of the digital images with
the respective second person, a third node representing the
respective identified person shown in at least one of the digital
images with the respective second person, each respective node
further comprising a visual representation of the respective count
of the total number of digital images where the respective
identified person appears with the respective second person; and a
link between the respective second node and each respective third
node.
[0237] Another non-limiting exemplary implementation could show the
relationships purely in list format based on the tiers away from
the central person. FIG. 47 shows how those photos may look
arranged by tiers.
[0238] Another non-limiting exemplary implementation for
representing the tiers may be to show a collage of photos contained
in that tier. FIG. 48 shows a possible display that demonstrates
this method.
[0239] Another non-limiting exemplary implementation of the present
invention for showing the data available from the name tags in
photos is to extract the face of a person from photos and display
those faces on a timeline. FIG. 49 provides an example. Starting
with a selected person (selected by the at least one computer or in
accordance with user input), all faces of that person may be
extracted from each photo in which they are tagged and displayed
along some form of date or time axis. If more than one photo of the
selected person is available, this may show the evolution of a
person over time.
[0240] In accordance with a non-limiting aspect of the present
invention, at least one computer may include or interface with a
database of portraits, each portrait associated with an identified
person shown in the respective portrait. The at least one computer
may select one of the identified persons from the database,
ordering a plurality of the portraits associated with the selected
identified person based at least partly on date metadata associated
with each respective portrait, and displaying a visual
representation. The visual representation may include a timeline;
and an arrangement of the plurality of the portraits along the
timeline in accordance with the respective ordering.
[0241] More details regarding use of face detection and recognition
for tagging purposes will now be described. Face detection
algorithms operate in many different ways, but the net result of
scanning a digital image and applying one or more of these
algorithms is the presentation of a list of possible face regions.
These regions are usually identified by a bounding box defined by
four coordinates (e.g. top/left, top/right, bottom/left,
bottom/right).
[0242] If a set of new photos is offered to a face detection system
of the present invention, then the system may return a list of
faces to the user and ask the user to tag the faces--in other
words--attach a name to the faces. This name could be input via the
keyboard or via drag and drop from an existing contact list or
address book. The user may also typically provide input to delete
any false positive faces where the system has falsely identified a
region of a photo as a face.
[0243] A face recognition system of the present invention may take
the face detection results (e.g. a list of bounding box coordinates
and a pointer to the original digital image) and generate digital
signatures for each face. These signatures are mathematical
representations of the pixels that constitute the face as defined
by the bounding box or other mask area applied to the bounding box
or a subset of the bounding box. For example, it may be possible to
use the eye coordinates of the face to center an oval mask over the
face region to attempt to eliminate non-specific features like
hair, hats, and other non-facial objects. Then only the pixels
within this mask area are used to generate the face signature.
[0244] These mathematical signatures could be a sequence of numbers
(real or complex) or a single digital string or a multi-dimensional
array depending on the algorithm. Other functions performed by a
face recognition system of the present invention may be to: [0245]
1. Compare two face signatures together to determine a "likeness"
score. If that score passes a dynamic or pre-defined threshold then
it becomes a "suggested" match between the two. [0246] 2. Combine
"like" face signatures together into clusters of faces that are
similar. This also uses a dynamic or pre-defined threshold to
determine whether face signatures belong together in the same
cluster.
[0247] An optional function is to find a representative signature
for a single person that has a list of face signatures based on
tagging activity by the user. In essence, this person is "known" to
the system because faces have been tagged with the same name.
[0248] In accordance with aspects of the present invention, the
present invention may enable the visualization of data
relationships extracted from photo metadata--specifically name tags
and dates that are attached to digital photos. This data could be
attached to photos directly via common metadata formats (e.g. EXIF,
Adobe XMP, etc.) or could exist separate from the photos in a
database that links the photo name to the metadata contained in
database tables.
[0249] Based on people identified via the tags (or in the database)
and the frequency of appearance and who they appear with in the
photos, data relationships are determined that can be graphed in
different ways. These graphs provide ways to visualize the
relationships.
[0250] The graphs may automatically be created by the invention or
the user of the invention may request a graph be created starting
with a specific person selected by the user. The specific person
would become the center of the graph in a network representation,
or left starting point in a "left to right" representation.
[0251] To collect the data required to display a graphic
representation like FIG. 46, multiple queries may have to be run
against a database containing metadata--specifically the name of
people appearing in photos. If the data only existed in the
metadata directly attached to the digital photos then this metadata
would have to be collected via an image scanning process. The
resulting collection could be placed in computer memory (such as an
array of text fields) or it could be placed into a permanent or
temporary SQL database for subsequent query purposes. For purposes
of this part of the description, assume that the data resides in a
SQL database.
[0252] The initial query would find a count for all images
containing people that appear with the selected "starting" person
in one or more photos. We'll call these people "acquaintances". The
resulting list could be sorted in descending order based on number
of appearances for each acquaintance. The graph would display the
starting person as a node in the graph. The size of the node may or
may not vary based on the number of photos. The number of photos in
this case would be the total number of photos in which the
"starting person" appears.
[0253] The next node displayed would be the first or largest
acquaintance in the list. The size of the node may or may not vary
with the number of times they appear in photos with the "starting
person". This node would be attached via a connector to the
original starting node. This process would continue until all the
acquaintances are exhausted in the list. One non-limiting
implementation would limit the size of the list to a minimum number
of appearances together. The graph could represent people that
appear in at least 3 photos with the starting person.
[0254] Once the list is exhausted, then for each acquaintance
another query would be run to determine all of the people that
appear together with that acquaintance but NOT with the starting
person. Based on this list then nodes would be added to the graph
with connectors back to the acquaintance.
[0255] This process would continue until all acquaintance nodes in
the graph are exhausted. In order to limit the size of the
graph--especially in the case of a person with tens of thousands of
photos--it is possible that the user could specify a limit of X
tiers away from the starting person.
[0256] The other types of graphs identified that show this "tier
relationship" would be generated in a similar fashion to the above,
but the method of display differs. For FIG. 47, the photos may be
shown as thumbnails or scrollable images on a "Tier by Tier" basis.
This could be a tabbed interface that offered a "tab per tier". For
FIG. 48, the photos are shown in a collage format for each tier
away from the central starting person. Each collage could be a
separate graph or page, or they could be combined into one large
graph.
[0257] To generate FIG. 49, the dates for the images may be used to
define the ordering of faces along the timeline. The face images
themselves may be extracted from the original photos using
coordinates for a pre-determined bounding box around the face. The
bounding box may be determined based on a face detection method
that identifies the coordinates containing a face automatically, or
the user may manually define a bounding box by physically drawing a
box around the face with the aid of a mouse, touchpad or touch
screen user interface.
[0258] In accordance with a non-limiting aspect of the present
invention, at least one computer may include or interface with a
database of digital images and respective metadata identifying a
name of at least one identified person shown in the respective
digital image. Each person may have been previously identified
using face detection and recognition techniques described herein.
The at least one computer may select one of the identified persons
from the database, determine at least one first identified person
shown in at least one of the digital images together with the
selected identified person, and display a visual representation.
The visual representation may include, for each first identified
person, a first tier node representing the selected identified
person and the respective first identified person being shown in at
least one of the digital images together. The visual representation
may also include, for each first identified person, a second tier
node representing the respective first identified person being
shown in at least one of the digital images without the selected
identified person.
[0259] Optionally, the visual representation may include, for each
first identified person, a second tier node representing the
respective first identified person being shown in at least one of
the digital images without the selected identified person, but with
a second identified person, wherein the second identifier person is
determined not to be shown in any of the digital images showing the
selected identified person.
[0260] Optionally, the visual representation may include, for each
second identified person, a third tier node representing the
respective second identified person being shown in at least one of
the digital images without the respective first identified
person.
[0261] Optionally, each first tier node may include a collage of
the digital images that show both the selected identified person
and the respective first identified person.
[0262] Optionally, each first tier node may include a collage of
digital images that show both the selected identified person and
the respective first identified person; each second tier node may
include a collage of the digital images that show the respective
first identified person without the selected identified person; and
each third tier node comprises a collage of the digital images that
show the second identified person without the respective first
identified person.
Clustering Description
[0263] An optional function of the present invention may be to find
a representative signature for a single person that has a list of
face signatures based on tagging activity by the user. In essence,
this person may be "known" to the system because faces have been
tagged with the same name. For purposes of determining a likeness
score and suggesting a match between two photos, the representative
signature is used to compare with each unidentified face to
determine if there is a match. The representative face signature
could also be a set of face signatures such that each item in the
set may represent a single cluster of face signatures for that
specific person.
[0264] Finding a representative face for a group of faces assigned
to a person may be useful in order to reduce the time required to
compare a large number of known faces (e.g. potentially hundreds of
known faces, or more) with each new unidentified face. Furthermore,
people age over time and change their appearance for a variety of
reasons so using a set of face signatures representing clusters of
similar faces for a particular person can provide better results
while at the same time reduce the number of comparisons necessary
versus comparing every specific known face.
[0265] A further use of clustering may be to present "like" faces
to the user in batches for tagging purposes. This may save time in
providing the ability to tag many faces with one name, instead of
tagging each individual face.
[0266] One challenge with clustering is that for performance
reasons it is not desirable to have to re-cluster the entire set of
unidentified faces as new faces are introduced to the set. It may
be more efficient to employ various techniques that allow for the
creation and management of incremental clusters.
[0267] Details will now be described regarding clustering for the
purposes of finding the representative faces for an individual
during the recognition step. The plurality of faces associated with
a known person may be grouped using a form of hierarchical
clustering, where face signatures are compared as described above,
to create groups of faces having similar appearance. To compare an
unknown face to the known person, all of the individual, one to
one, face comparisons may be made, and then the results may be
combined by arithmetic mean to form one or more aggregate results,
one for each group of similar faces.
[0268] To enable effective comparison of the aggregate results a
linear correction which depends on the number of individual results
used to compute the aggregate result may be applied. This linear
correction may be determined by first comparing a group of known
faces having a particular number, "N", with a large number of
individual faces known to belong to different people to determine
the distribution of results.
[0269] Then the linear correction may be determined to be that
which best aligns this distribution to a standard normal
distribution. Finally, the correction may be further adjusted so as
to give larger groups of faces an advantage in the comparison with
unknown faces by either increasing the standard deviation or
shifting the mean. This final step ensures that people who appear
frequently in a user's collection of photos have an appropriate
increase in their likelihood of being suggested as a match for a
new unknown face.
[0270] The plurality of corrected aggregate results obtained by
comparison of a single unknown face to all of the known persons may
be compared against either a fixed threshold or a dynamically
chosen threshold. If a dynamically chosen threshold is to be used,
it may be selected to be the threshold that yields the maximum
number of unknown faces for which only a single person has results
exceeding the threshold. The advantage of such a dynamically chosen
threshold is that it maximizes the number of true person
suggestions while simultaneously minimizing false positive
suggestions.
[0271] In one embodiment of the invention this clustering process
could employ a similar method to what is described under the term
"hierarchical clustering" in Wkipedia (See:
http://en.wikipedia.org/wiki/Hierarchical_clustering). The initial
clustering may be "agglomerative" (a bottom up approach where each
observation starts with its own cluster, and pairs of clusters are
merged as one moves up the hierarchy) while the selection of
representative faces may be "divisive" (a top down approach where
all observations start in one cluster, and splits are performed
recursively as one moves down the hierarchy). Initially every face
is considered to be its own separate cluster. Each face is compared
to every other face and the score (likeness) values are recorded
and sorted. Scores below a certain pre-determined threshold are
discarded. Then, starting with the highest score (the two faces
that are most alike) and working towards the lowest score, each
pair of faces are joined by an edge if they are not already joined
via some path of edges. That is, two separate clusters are merged
but faces within a cluster are not further joined by additional
edges. The result is several distinct dendrograms (trees), each one
representing a cluster. Note that within each dendrogram there are
no loops and the number of edges is one less than the number of
faces. For each cluster where the number of faces is larger than
some specific integer threshold (e.g. 20), we select a subset of
the faces to represent the cluster.
[0272] Selection of the subset could be implemented as follows.
First find the edge with the lowest score and remove it to split
the tree into two smaller subtrees. Then decide how many faces will
be selected from each subtree so that the counts are in equal
proportion to the size of the subtree. For example, if we are
looking for 15 faces and subtree A is twice as big as subtree B,
then we will want to select 10 faces from subtree A and 5 faces
from subtree B. If the number of faces in the subtree is equal to
the number we want, then those faces are simply output as the
result and processing of that subtree terminates. If the number of
faces is greater than the number we want, this process is applied
again recursively. That is, within the subtree, the next edge with
the lowest score is found and removed to further divide the tree.
The result is the reduction of a tree of any size to some fixed
size such that the remaining faces are a statistically
representative sample of the original set of faces.
[0273] Details will now be described regarding the use of
clustering for unidentified faces to improve the time involved for
tagging by reducing the number of clicks required by the user. For
practical and performance reasons, in accordance with an aspect of
the present invention, a fully incremental constant time clustering
algorithm was created. At least one advancement of this clustering
algorithm may be the constant time aspect, which does not vary
depending on total number of photos in the set. To describe the
steps involved in adding a new face to the existing clustering,
first assume that each of the existing faces has been assigned some
sort of clustering token (e.g. an integer). Two faces are
considered to be in the same cluster if they have the same token.
Second, assume that each face is associated with some photo and
that each photo has a date and time associated with it. This is
usually but not always the date and time that the photo was taken.
Finally, the photos are kept in a sorted order by this date and
time metadata.
[0274] Given a new photo with at least one face in it, first use
the date/time of the new photo to find its location within the
sorted list of existing photos. Then start performing one-to-one
face comparisons between the new face and existing faces by moving
outward in both directions from this location. That is, the new
face is first compared to existing faces that were photographed at
a similar date/time before moving to progressively more distant
(past and future) dates.
[0275] If a one-to-one face comparison yields a likeness that
exceeds some fixed threshold, then the new face is assigned the
same cluster token as the existing face and the clustering of the
new face is complete.
[0276] Three rules may be applied to ensure that the time spent
adding each new face to the clustering is constant. First, new
faces are never compared to other new faces in the same photo.
Second, if the new face fails in its comparison to one face of a
particular cluster, then the new face is not compared against any
other faces from that same cluster. Third, we now have a fixed
maximum number of comparisons that will be made. If this number is
reached without finding a match, then the new face will be assigned
a new cluster token and will, therefore, be the first face in a new
cluster.
[0277] Finally, when processing a batch of photos, the photos are
processed in a random order to improve "long range" clustering.
[0278] FIG. 50 shows photos and faces with associated dates and
times, arranged chronologically. The "New Photo" is being analyzed
in accordance with the present invention. The letters on each face
represent the cluster id/token for the respective face. The curved
lines with arrows shown below the cluster tokens in FIG. 50
represent the face-to-face comparisons that are to be performed
from faces in the New Photo to faces in other photos. The numerical
value shown on the curved lines representing the face-to-face
comparisons indicates the order in which the respective
face-to-face comparisons are performed. For example, photos with
the closest date/time to the New Photo may be compared first, and
the remaining faces may be compared in chronological order of the
associated dates and times of the photos or faces. Assuming no
matches were found in earlier face-to-face comparisons, the three
existing faces with no curved lines linking the respective faces to
the face from the New Photo, indicate that comparisons with those
respective faces were skipped, as the respective faces belonged to
a cluster already considered.
[0279] In order to show a match, the comparisons cease when a match
is found and the new face takes on the same cluster id letter as
the matching face (e.g. "E"), shown in FIG. 50.
[0280] In order to show no-match, then the new face may get
assigned a new cluster id (e.g. "F"), not shown in FIG. 50.
[0281] In accordance with a non-limiting aspect of the present
invention, at least one computer may include or interface with a
database of digital images and respective metadata identifying a
date of the respective digital image, a plurality of the digital
images showing at least one respective unidentified person
("unidentified digital images"). The at least one computer may sort
the unidentified digital images by the respective date metadata,
and assign a respective clustering token to each of the
unidentified digital images. The assigning may include, in
accordance with a determination that a subset of the unidentified
digital images each show a common unidentified person, assigning a
common respective clustering token to each of the unidentified
digital images of the subset. The at least one computer may group
the unidentified digital images by respective clustering token. At
some point, the at least one computer may receive a new digital
image from another computer, database, user, or from anywhere else.
The new digital image and respective metadata may identify a date
of the respective new digital image, and the new digital image may
include a new unidentified person. The at least one computer may
then attempt to identify the new unidentified person in the
received image by using or leveraging any of the clustering
techniques described herein. In particular, the at least one
computer may perform at least one comparison of the new
unidentified person to the at least one respective unidentified
person of the plurality of the digital images in an order, wherein
for each group of unidentified digital images, the at least one
computer may perform only a single comparison of the new
unidentified person to the respective common unidentified person.
The at least one computer may assign a clustering token to the new
digital image in accordance with the comparison performing
resulting in a determination of the new unidentified person common
to a respective one of the groups of unidentified digital images.
Optionally, the comparison performing order comprises an order by
date metadata closest in date to the date metadata of the new
digital image. Optionally, wherein upon having performed a
predetermined maximum number of comparisons of the new unidentified
person to the at least one respective unidentified person of the
plurality of the digital images, the at least one computer may halt
the comparison performing and assigning a new clustering token to
the new digital image.
[0282] FIGS. 51-53 illustrate aspects of the faces clustering of
the present invention. FIG. 51 shows faces of a respective known
person may be grouped through tagging methods provided by the
present invention. In FIG. 51, groups of faces of known persons A,
B, and C, are respectively grouped. In FIG. 52, a recognition
algorithm in accordance with the present invention may the groups
of faces of a known person into one or more clusters of faces of
the known person. The splitting into clusters of like faces may be
based at least partly on the face signature distance between each
face in the group of faces of the known person. In FIG. 53, when an
unknown face is submitted to the face recognition method of the
present invention, the recognition algorithm of the present
invention may compare the unknown face with each cluster for each
known person separately.
Using Image Metadata to Improve Face Recognition Results
[0283] A challenge in face recognition may be that due to the
unconstrained nature of consumer photos taken with a wide range of
camera devices--including smart phones, cell phones, and disposable
cameras--the ability to accurately identify people in photos (or
video) will likely never reach 100% accuracy. There are just too
many variables similar to the challenges associated with accurate
weather forecasting.
[0284] It is possible to enhance the accuracy of recognition
results beyond the pure mathematics of analyzing and comparing
pixels contained in the image. By taking advantage of image related
metadata including date taken, camera type, location coordinates,
and event information it is possible to reduce false positive data
generated from the face recognition algorithms. In a further
extension of this concept it is also possible to recognize other
objects in the image (non-human) that could relate to a specific
place, event or time.
[0285] These concepts also apply to video images in addition to
static images. They can be applied in real-time as the user is
taking the photo or video; or they can be applied post-image
taking.
[0286] A non-limiting implementation of this concept in accordance
with an aspect of the present invention in shown in FIG. 40. Face
recognition algorithms operate on a list of unidentified faces and
compare the digital signatures for those faces against digital
signatures for known faces. Any faces from the unidentified set
that come within a pre-defined (or algorithmic) threshold with a
known face will become "suggested matches" for the person linked to
the known face. If a single photo generated five face regions from
the face detection scan, then it may be inefficient to have the
same person offered as a suggestion for more than one face in the
same photo. Note that there could be special circumstances (e.g.
photos involving a mirror and photos modified with an editor) where
the same person could appear more than once in a single photo,
however it is not practical to design a system that satisfies these
rare corner cases. Thus, if the face recognition algorithm came up
with two (or more) faces from the same image as a suggestion for
the unidentified person, then further logic would consider the fact
that the two (or more) faces are in the same image and request the
"most closest match" to be the suggestion used. Should the most
closest match that is suggested to the user be rejected by the
user, in order to improve efficiency, it may be desirable to, with
respect to any faces not the most closest match from that image,
and thus not suggested, discarded those faces from being suggested
in any other digital images as well. Any rejected face(s)
suggestions may then be compared against the set of known people
excluding the unidentified person to find the next best match (if
any).
[0287] In accordance with a non-limiting aspect of the present
invention, at least one computer may include or interface with a
database of portraits and associated digital images, each portrait
associated with an identified person shown in the respective
portrait. The at least one computer may determine that respective
portraits of at least two identified persons shown in one of the
digital images satisfy a comparison threshold with a portrait of
the unidentified person determined from the received digital image.
The at least one computer may then suggest an identification of the
unidentified person as the respective one of the at least two
identified persons having a respectively associated portrait that
is determined to be a closest match to the portrait of the
unidentified person from amongst the at least two identified
persons. The at least one computer may then exclude a remainder of
the at least two identified persons from being subsequently
suggested from any other one of the digital images as an
identification of the unidentified person.
[0288] Dates or times may also be considered when suggesting faces,
as shown in FIG. 41. It should be assumed that the metadata (e.g.
dates, coordinates, camera type, etc.) related to the images being
used for the present invention is accurate, otherwise basing face
suggestions based on the metadata would not be expected to increase
accuracy. Consider a person, referred to as P, tagged in a photo
that shares the same date as another photo. In the other photo,
assume there exists an unidentified face where two (or more) people
meet the recognition threshold for calling that face a suggested
match for those people. Then if one of those people that meet the
threshold happens to be confirmed by the user to be person P, the
present invention may increase the ranking of person P in a list of
suggested identifications based on the probability that person P
will show up again in photos from the same date after already
having been tagged and confirmed to be present on that date in
another photo.
[0289] In accordance with a non-limiting aspect of the present
invention, at least one computer may include or interface with a
database of portraits and associated digital images, each portrait
associated with an identified person shown in the respective
portrait. The at least one computer may determine that a respective
portrait of at least one identified person shown in at least one of
the digital images satisfies a comparison threshold with a portrait
of the unidentified person determined from the received digital
image. In accordance with the determined at least one of the
digital images associated with metadata comprising a date
corresponding to date metadata associated with the received digital
image, the at least one computer may suggest an identification of
the unidentified person as the at least one identified person.
[0290] Relationships with people may also be considered, as shown
in FIG. 42. Assume a person, referred to as P, is tagged in a photo
and other unidentified faces are found in that photo. Further
assume that person P also belongs to a group, referred to as C, and
this group is known to the system. Then as part of the recognition
suggestion algorithm, the fact that the probability of other faces
in the photo also being members of group C is higher could be
considered in the method by reducing the threshold for each person
that is a member of group C, or moving a person from group C higher
in the list of potential matches if there is more than one person
meeting the suggestion threshold for a given unidentified face.
[0291] In accordance with a non-limiting aspect of the present
invention, at least one computer may include or interface with a
database of portraits and associated digital images, each portrait
associated with an identified person shown in the respective
portrait. The at least one computer may, in accordance with a
determination that the received photo comprises at least one
identified person associated with a defined group, suggest an
identification of the unidentified person based at least partly on
a determination that a respective portrait of at least one
identified person associated with the defined group satisfies a
comparison threshold with a portrait of the unidentified person
determined from the received digital image.
[0292] Locations or location coordinates may also be considered, as
shown in FIG. 43. Assume a person P is tagged and confirmed to be
in a photo in location X on date A, and, per the recognition
algorithm, person P would normally be a suggested match for
unidentified faces in other photos also taken on date A, but in
location Y, where Y is more than N hours away from X. In this case,
the suggested match for person P may be discarded on the basis that
person P could not have been, or was unlikely to have been, in both
locations X and Y within the date timeframe established by the
metadata of the respective photos. This may take into account the
probability that person P cannot be in two places at the same
time.
[0293] In accordance with a non-limiting aspect of the present
invention, at least one computer may include or interface with a
database of portraits and associated digital images, each portrait
associated with an identified person shown in the respective
portrait. The at least one computer may determine that a respective
portrait of at least one identified person shown in at least one of
the digital images satisfies a comparison threshold with a portrait
of the unidentified person determined from the received digital
image. In accordance with the determined at least one of the
digital images associated with metadata comprising both (i) a date
corresponding to date metadata associated with the received digital
image; and (ii) a location within a predetermined distance
threshold of location metadata associated with the received digital
image; the at least one computer may suggest an identification of
the unidentified person as the at least one identified person.
[0294] Event information, or other information available on a
social network, may also be considered, as shown in FIG. 44. Assume
the system knows the person P is attending an event X based on
social network information captured for that user or other data
source, then the probability that person P will appear in photos
taken at event X is higher. It is also reasonable to assume that
the probability that person P will show up in photos taken at event
Y, taking place on the same date, is lower. The recognition method
could utilize this further information to assign person P a higher
ranking (via one or more specific adjustments) for faces found in
photos taken at event X where P would otherwise meet a threshold
for being suggested as any of the unidentified faces. The
recognition method could also assign person P a lower ranking (via
one or more specific adjustments) for faces found in photos taken
at event Y.
[0295] In accordance with a non-limiting aspect of the present
invention, at least one computer may include or interface with a
database of portraits and associated digital images, each portrait
associated with an identified person shown in the respective
portrait. The at least one computer may determine that a respective
portrait of at least one identified person shown in at least one of
the digital images satisfies a comparison threshold with a portrait
of the unidentified person determined from the received digital
image. The at least one computer may also determine that the
received digital image is associated with an event, based at least
partly on metadata associated with the received digital image. In
accordance with the determined at least one of the digital images
being associated with the event, the at least one computer may
suggest an identification of the unidentified person as the at
least one identified person.
[0296] Non-human object or environmental information may also be
considered, as shown in FIG. 45. For example, assume that a person
P was confirmed and tagged in a photo, and that photo was deemed to
be part of a set X of similar photos based on date, event, or
location information. Then if person P was further found in the
confirmed photo using an object recognition method to be wearing a
green sweater, then that information could be used to apply a
higher priority or ranking to person P if a face in another photo
from set X was found also to be attached to a green sweater. In
addition to clothing, other objects shown in the photo, such as
furniture, trees, cars, animals, clouds, vistas, or any other
environmental arrangements of photographic details may be analyzed
and considered. The color, texture, orientation, or arrangement of
any such details may be analyzed and considered. Particularly, if
person P tagged in a photo of a set based on date, event, or
location, then non-face details of person P may be compared to
suggest that person P is an unidentified person in other photos of
the set (non-face details may include clothing shape or color, hair
color, glasses, skin tone, etc.)
[0297] In accordance with a non-limiting aspect of the present
invention, at least one computer may include or interface with a
database of portraits and associated digital images, each portrait
associated with an identified person shown in the respective
portrait. The at least one computer may determine that a respective
portrait of at least one identified person shown in at least one of
the digital images satisfies a comparison threshold with a portrait
of the unidentified person determined from the received digital
image. The at least one computer may then extract non-portrait
visual information from the determined at least one of the digital
images. In accordance with a determination of the extracted
non-portrait visual information satisfying a comparison threshold
with non-portrait visual information from the received digital
image, the at least one computer may suggest an identification of
the unidentified person as the at least one identified person.
Voice-Assisted Face Tagging
[0298] Optionally, voice or speech recognition may be used to
assist or support face tagging as provided in aspects of the
present invention. The at least one computer may prompt the user to
tag photos. The user may respond by providing user input using any
available means, such as by clicking with a mouse, touchpad, key
entry, or by responding with voice input. The user may be
interfacing with a local computing device, upon which the face
tagging method is operating, or the user may be interfacing with a
computer in communication with one or more other computers or
computer servers, that alone or together provide for the tagging of
photos or faces. In either case, the local computing device with
which the user is interfacing, which may be a mobile phone,
personal computer, or any other type of computing device, may
include or be connected to, either through a wired or wireless
connection, a microphone or other audio input peripheral. The local
computing device, or one of the other computers in communication
therewith, may process the user's audio input to determine one or
more voice commands.
[0299] Once the tagging process has begun, and voice recognition is
supported and enabled, the user may be presented with one or more
photos to tag. Optionally, the user may be asked whether the user
recognizes a particular photo. One of the computers may have
attempted to determine a date or time of the photo. The user may be
prompted to either confirm or modify this date or time. A voice
command may be provided from the user in response to be processed
by one of the computers. Through the face recognition methods
described in accordance with aspects of the present invention,
where at least one person is determined to be found in the photo,
the user may be prompted to identify the at least one person. A
voice command may be provided from the user in response identifying
the person by name. Where the voice command does not provide a full
name for the person, the face recognition method may suggest a full
name to be used to tag the person based on the voice command
received. For example, if the user identifies the person as "Bob",
the method may suggest at least one full name for "Bob" based on
other persons named "Bob" tagged in other photos by the user, or
based on other persons named "Bob" who are connected to the user
through a social network or contact list. This process may repeat
for all of the persons in the photo, and for any other photos not
yet tagged by the user. Optionally, existing photos already tagged
may also be re-presented to the user for tagging or re-tagging,
preferably after all untagged photos have been processed and tagged
in accordance with the method of the present invention. The method
may also prompt the user to record a caption for any photos being
tagged. Any recorded caption may be played back for the user to
confirm prior to applying to the respective photo. The caption may
also be processed through voice recognition techniques and saved as
a text caption associated with the respective photo. The caption
text may also be presented to the user for review or confirmation
prior to saving.
General
[0300] It will be appreciated that any module or component
exemplified herein that executes instructions may include or
otherwise have access to computer readable media such as storage
media, computer storage media, or data storage devices (removable
and/or non-removable) such as, for example, magnetic disks, optical
disks, tape, and other forms of computer readable media. Computer
storage media may include volatile and non-volatile, removable and
non-removable media implemented in any method or technology for
storage of information, such as computer readable instructions,
data structures, program modules, or other data. Examples of
computer storage media include RAM, ROM, EEPROM, flash memory or
other memory technology, CD-ROM, digital versatile disks (DVD),
blue-ray disks, or other optical storage, magnetic cassettes,
magnetic tape, magnetic disk storage or other magnetic storage
devices, or any other medium which can be used to store the desired
information and which can be accessed by an application, module, or
both. Any such computer storage media may be part of the mobile
device, tracking module, object tracking application, etc., or
accessible or connectable thereto. Any application or module herein
described may be implemented using computer readable/executable
instructions that may be stored or otherwise held by such computer
readable media.
[0301] Thus, alterations, modifications and variations can be
effected to the particular embodiments by those of skill in the art
without departing from the scope of this disclosure, which is
defined solely by the claims appended hereto.
[0302] In further aspects, the disclosure provides systems,
devices, methods, and computer programming products, including
non-transitory computer readable memory, or non-transient
machine-readable instruction sets, for use in implementing such
methods and enabling the functionality described previously.
[0303] Although the disclosure has been described and illustrated
in exemplary forms with a certain degree of particularity, it is
noted that the description and illustrations have been made by way
of example only. Numerous changes in the details of construction
and combination and arrangement of parts and steps may be made.
Accordingly, such changes are intended to be included in the
invention, the scope of which is defined by the claims.
[0304] Except to the extent explicitly stated or inherent within
the processes described, including any optional steps or components
thereof, no required order, sequence, or combination is intended or
implied. As will be will be understood by those skilled in the
relevant arts, with respect to both processes and any systems,
devices, etc., described herein, a wide range of variations is
possible, and even advantageous, in various circumstances, without
departing from the scope of the invention, which is to be limited
only by the claims.
* * * * *
References