U.S. patent application number 12/919092 was filed with the patent office on 2010-12-30 for networked face recognition system.
This patent application is currently assigned to C-TRUE LTD.. Invention is credited to Ester Freitsis, Avihu Meir Gamliel, Shmuel Goldenberg, Yuri Kheifetz, Felix Tsipis.
Application Number | 20100329568 12/919092 |
Document ID | / |
Family ID | 41465533 |
Filed Date | 2010-12-30 |
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United States Patent
Application |
20100329568 |
Kind Code |
A1 |
Gamliel; Avihu Meir ; et
al. |
December 30, 2010 |
Networked Face Recognition System
Abstract
A networked system for face recognition, the system comprising:
a face verifier, configured to verify compliance of a face in an
image with at least one predefined criterion, and upon successful
verification of the compliance, to forward the image for feature
extraction, a feature extractor, associated with the face verifier,
configured to extract a feature from the forwarded image; and a
face identifier, communicating with the feature extractor over a
network, configured to receive the extracted feature and identify
the face in the forwarded image, using the extracted feature.
Inventors: |
Gamliel; Avihu Meir;
(Pardes-Hana, IL) ; Goldenberg; Shmuel;
(Ness-Ziona, IL) ; Tsipis; Felix; (Ma'alei Adomim,
IL) ; Kheifetz; Yuri; (Tel-Aviv, IL) ;
Freitsis; Ester; (Ashdod, IL) |
Correspondence
Address: |
The Law Office of Michael E. Kondoudis
888 16th Street, N.W., Suite 800
Washington
DC
20006
US
|
Assignee: |
C-TRUE LTD.
Rehovot
IL
|
Family ID: |
41465533 |
Appl. No.: |
12/919092 |
Filed: |
June 24, 2009 |
PCT Filed: |
June 24, 2009 |
PCT NO: |
PCT/IB2009/052722 |
371 Date: |
August 24, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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61133711 |
Jul 2, 2008 |
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Current U.S.
Class: |
382/190 |
Current CPC
Class: |
G06K 9/00281 20130101;
G06K 9/00979 20130101; G06K 9/00241 20130101 |
Class at
Publication: |
382/190 |
International
Class: |
G06K 9/46 20060101
G06K009/46 |
Claims
1. A networked system for face recognition, the system comprising:
a face verifier, configured to verify compliance of a face in an
image with at least one predefined criterion, and conditioned upon
compliance of the face with a predefined criterion, to forward the
image for feature extraction; a feature extractor, associated with
said face verifier, configured to extract a feature from the
forwarded image; and a face identifier, communicating with said
feature extractor over a network, configured to receive the
extracted feature and identify the face in the forwarded image,
using the extracted feature.
2. The networked system of claim 1, wherein said network is a wide
area network.
3. The networked system of claim 1, wherein said network is the
internet.
4. The networked system of claim 1, wherein the predefined
criterion is indicative of symmetry of the face.
5. A networked system for face recognition, the system comprising:
a face verifier, configured to verify compliance of a face in an
image with at least one predefined criterion, and conditioned upon
compliance of the face with a predefined criterion, to send the
image over a network; a feature extractor, communicating with said
face verifier over the network, and configured to receive said sent
image and extract a feature from said received image; and a face
identifier, associated with said feature extractor and configured
to identify the face in the received image, using the extracted
feature.
6. The networked system of claim 5, wherein said network is a wide
area network.
7. The networked system of claim 5, wherein said network is the
Internet.
8. A networked system for face recognition, the system comprising:
a face verifier, configured to verify compliance of a face in an
image with at least one predefined criterion, and conditioned upon
compliance of the face with a predefined criterion, to send the
image over a first network; a feature extractor, communicating with
said face verifier over the first network, configured to receive
said sent image, extract a feature from said received image, and
sent said extracted feature over a second network; and a face
identifier, communicating with said feature extractor over the
second network, configured to receive the extracted feature and
identify the face in the received image, using the extracted
feature.
9. The networked system of claim 8, wherein at least one of said
first and second network is a wide area network.
10. The networked system of claim 8, wherein at least one of said
first and second network is the internet.
11. A networked system for face recognition, the system comprising:
a face verifier, configured to verify compliance of a face in an
image with at least one predefined criterion, and conditioned upon
compliance of the face with a predefined criterion, to send data
comprising at least a part of the image over a network; and a face
identifier, communicating with said face verifier over the network,
configured to receive the sent data and identify the face, using at
least a part of the received data.
12. The networked system of claim 11, wherein said network is a
wide area network.
13. The networked system of claim 11, wherein said network is the
internet.
14. The networked system of claim 11, wherein said network is an
intranet network.
15. The networked system of claim 11, further comprising an image
capturer, associated with said face verifier, and configured to
capture said image of said face.
16. The networked system of claim 11, further comprising a face
detector, associated with said face verifier, and configured to
detect said face in said image.
17. The networked system of claim 16, wherein said face detector is
further configured to use a skin detection method, for detecting
said face in said image.
18. The networked system of claim 16, wherein said face detector is
further configured to use a Viola-Jones detection method, for
detecting said face in said image.
19. The networked system of claim 16, wherein said face detector is
further configured to use a Gabor Filter based method, for,
detecting said face in said image.
20. The networked system of claim 11, further comprising an image
cropper, associated with said face verifier, and configured to crop
said image, thereby to remove background from said image.
21. The networked system of claim 11 further comprising an image
resizer, associated with said face verifier, and configured to
resize said image into a predefined size.
22. The networked system of claim 11, further comprising an image
illumination quality improver, associated with said face verifier,
and configured to improve a quality of illumination of said
image.
23. The networked system of claim 22, wherein said image
illumination quality improver is further configured to use
Histogram Equalization, for improving said quality of illumination
of said image.
24. The networked system of claim 11, wherein said face verifier is
further configured to use an intensity map, for verifying the
compliance of the face with the predefined criterion.
25. The networked system of claim 11, wherein said face verifier is
further configured to use a gradient map, for verifying the
compliance of the face with the predefined criterion.
26. The networked system of claim 11, wherein said face verifier is
further configured to use a Fourier Transform phase map, for
verifying the compliance of the face with the predefined
criterion.
27. The networked system of claim 11, wherein said face verifier is
further configured to measure said compliance of said face in each
one of a plurality of input images, and select at least one image
of said face among said plurality of input images, and wherein said
measured compliance of said face in said at least one selected
image of said face is highest amongst said input images.
28. The networked system of claim 27, wherein said plurality of
input images are at least a part of a video sequence.
29. A networked system for face recognition, the system comprising:
a face verifier, configured to verify compliance of a face in an
image with at least one predefined criterion, and conditioned upon
compliance of the face with a predefined criterion, to send data
comprising at least a part of the image over a network; and a face
database updater, communicating with said face verifier over the
network, and configured to receive the sent data and update a face
database with at least a part of the received data.
30. The networked system of claim 29, wherein said network is a
wide area network.
31. A networked system for face recognition, the networked system
comprising: a face verifier, configured to verify compliance of a
face in an image with at least one predefined criterion, and
restrict forwarding of data comprising at least a part of the image
over a network, according to results of the verification of the
compliance.
32. Method for face recognition, the method comprising: a)
verifying compliance of a face in an image with a predefined
criterion; b) sending data comprising at least a part of the image
over a network, for identification, said sending conditioned upon
compliance of the face with the predefined criterion; and c)
identifying the face in the image, using at least a part of the
sent data.
33. The method of claim 32, further comprising capturing said image
of said face.
34. The method of claim 32, further comprising detecting said face
in said image.
35. The method of claim 32, further comprising using a skin
detection method, for detecting said face in said image.
36. The method of claim 32, further comprising using a Viola-Jones
detection method, for detecting said face in said image.
37. The method of claim 32, further comprising using a Gabor Filter
based method, for detecting said face in said image.
38. The method of claim 32, further comprising cropping said image,
thereby removing background from said image.
39. The method of claim 32, further comprising resizing said image
into a predefined size.
40. The method of claim 32, further comprising improving a quality
of illumination of said image.
41. The method of claim 40, further comprising using Histogram
Equalization, for improving said quality of illumination of said
image.
42. The method of claim 32, further comprising using an intensity
map, for verifying the compliance with the predefined
criterion.
43. The method of claim 32, further comprising using a gradient
map, for verifying the compliance with the predefined
criterion.
44. The method of claim 32, further comprising using a Fourier
Transform phase map, for verifying the compliance with the
predefined criterion.
45. The method of claim 32, further comprising measuring said
compliance of said face in each one of a plurality of input images,
and selecting at least one image of said face among said plurality
of input images, and wherein said measured compliance of said face
in said at least one selected image of said face is highest amongst
said input images.
46. The method of claim 45, wherein said plurality of input images
are at least a part of a video sequence.
47. Method for face recognition, the method comprising: a)
verifying compliance of a face in an image with a predefined
criterion; b) sending data comprising at least a part of the image
over a network, said sending conditioned upon compliance of the
face with a predefined criterion; and c) updating a database of
images with at least a part of the sent data.
48. The method of claim 47, wherein said network is a wide area
network.
49. Method for face recognition, the method comprising: a)
verifying compliance of a face in an image with a predefined
criterion; and b) controlling forwarding of data comprising at
least a part of the image through a network, according to a result
of said verifying of the compliance.
Description
FIELD AND BACKGROUND OF THE INVENTION
[0001] The present invention relates to face authentication and
recognition and, more particularly, but not exclusively to a
networked system for automatic and remote face authentication and
recognition.
[0002] In recent years, identity theft has become one of the
fastest growing crimes in the world.
[0003] Identity theft is a criminal fraud that involves someone
pretending to be someone else in order to steal money or get other
benefits. A person whose identity is used can suffer various
consequences when he or she is held responsible for the
perpetrator's actions.
[0004] Identity theft includes, but is not limited to
business/commercial identity theft (using another's business name
to obtain credit), criminal identity theft (posing as another when
apprehended for a crime), financial identity theft (using another's
identity to obtain goods and services), identity cloning (using
another's information to assume his or her identity in daily life),
and medical identity theft (using another's information to obtain
medical care, drugs, or access to sensitive medical records).
[0005] In many countries specific laws make it a crime to use
another person's identity for personal gain. However, neither laws
nor traditional authentication methods (such as passwords or
identity cards) have proved useful in preventing identity theft by
sophisticated criminals.
[0006] Many institutions turned to biometric methods for prevention
of identity theft crimes.
[0007] For example, U.S. Pat. No. 5,930,804, to Yu et al., filed on
Jun. 9, 1997, describes a method for biometric authentication of
individuals involved in transactions employing the Internet.
[0008] Many governments and international organizations have chosen
face recognition as a primary biometric identification method, to
base systems for prevention of identity theft crimes on, as well as
for other cases where authentication of a person's identity is
crucial, say for controlling access to classified information.
[0009] In recent years, automatic face recognition is in rapid
growth, due to computational and algorithmic improvements, growth
in need for authentication or verification in the "global village"
and the need for preventing frauds. Given a growing need to ease
password management, and to use control service access (such as web
bank accounts access, medical personal information access, and
native access control services), border control and ID services,
face recognition has become one of the promising and preferred
technologies. The growing popularity of face recognition also stems
from the non-intrusiveness of face recognition, and from face
recognition's being easy to use and relatively free of regulative
constraints.
[0010] For example, U.S. Pat. No. 7,050,608, to Dobashi, filed on
Mar. 7, 2002, entitled "Face image recognition apparatus",
discloses a face image recognition apparatus. Dobashi's face image
recognition apparatus includes a registration information holding
section in which a reference feature amount of the face of at least
one to-be-recognized person is previously registered.
[0011] The feature amount of the face is extracted from a face
image input via an image input section by use of feature amount
extracting section. A recognition section determines the
recognition rate between the extracted feature amount and the
reference feature amount registered in the registration information
holding section. A feature amount adding section additionally
registers the feature amount extracted by the feature amount
extracting section as a new reference feature amount into the
registration information holding section when it is determined that
the determined recognition rate is lower than a preset value.
[0012] U.S. Pat. No. 7,221,809, to Geng, filed on Dec. 17, 2002,
entitled "Face recognition system and method", discloses a method
of automatically recognizing a human face. The method described by
Geng includes developing a three-dimensional model of a face, and
generating a number of two-dimensional images based on the
three-dimensional model. The generated two-dimensional images are
then enrolled in a database and searched against an input image to
identifying the face of the input image.
[0013] Security screening involves capturing images of people in
public places and comparing them to images of persons who are known
to pose security risks. One prime example of security screening is
its use at airport security checkpoints.
[0014] For example, U.S. Pat. No. 5,164,992, to Turk, filed on Nov.
1, 1990, entitled "Face Recognition System", describes a
recognition system for identifying members of an audience.
[0015] The system described by Turk includes an imaging system
which generates an image of the audience and a selector module for
selecting a portion of the generated image. Turk's system further
includes a detection means which analyzes the selected image
portion to determine whether an image of a person is present, and a
recognition module responsive to the detection means for
determining whether a detected image of a person identified by the
detection means resembles one of a reference set of images of
individuals.
[0016] U.S. patent application Ser. No. 10/719,792, to Monroe,
filed on Nov. 21, 2003, entitled "Method for incorporating facial
recognition technology in a multimedia surveillance system",
discloses facial recognition technology integrated into a
multimedia surveillance system for enhancing the collection,
distribution and management of recognition data by utilizing the
system's cameras, databases, monitor stations, and notification
systems.
[0017] U.S. patent application Ser. No. 11/450,581, to Chen et al.,
filed on Jun. 12, 2006, entitled "Three-dimensional face
recognition system and method ", describes a three dimensional (3D)
face recognition system.
[0018] Chen's system has a first data storing module for storing
three dimensional (3D) face model data and two dimensional (2D)
face image data, an input unit for inputting 3D face model data and
2D face image data, a signal conversion module for converting
analog data of the 3D face model data and 2D face image data to
digital data, and a second data storing module for storing the
digital data.
[0019] Chen's system further includes a micro-processing module for
analyzing geometric characteristics of points in the 3D face model
data stored in the first and second data storing module to
determine feature points of the 3D face model data, and assigning
different weight ratios to feature points. Chen's system further
includes a comparison module for comparing the feature points
stored in the first and second data storing module, during which
different geometric characteristics being given different weight
ratios, and calculating relativity between the feature points to
obtain a comparison result.
SUMMARY OF THE INVENTION
[0020] According to one aspect of the present invention there is
provided a networked system for face recognition, the system
comprising: a face verifier, configured to verify compliance of a
face in an image with at least one predefined criterion, and upon
successful verification of the compliance, to forward the image for
feature extraction; a feature extractor, associated with the face
verifier, configured to extract a feature from the forwarded image;
and a face identifier, communicating with the feature extractor
over a network, configured to receive the extracted feature and
identify the face in the forwarded image, using the extracted
feature.
[0021] According to a second aspect of the present invention there
is provided a networked system for face recognition, the system
comprising: a face verifier, configured to verify compliance of a
face in an image with at least one predefined criterion, and upon
successful verification of the compliance, to send the image over a
network; a feature extractor, communicating with the face verifier
over the network, and configured to receive the sent image and
extract a feature from the received image; and a face identifier,
associated with the feature extractor and configured to identify
the face in the received image, using the extracted feature.
[0022] According to a third aspect of the present invention there
is provided a networked system for face recognition, the system
comprising: a face verifier, configured to verify compliance of a
face in an image with at least one predefined criterion, and upon
successful verification of the compliance, to send the image over a
first network; a feature extractor, communicating with the face
verifier over the first network, configured to receive the sent
image, extract a feature from the received image, and sent the
extracted feature over a second network; and a face identifier,
communicating with the feature extractor over the second network,
configured to receive the extracted feature and identify the face
in the received image, using the extracted feature.
[0023] According to a fourth aspect of the present invention there
is provided a networked system for face recognition, the system
comprising: a face verifier, configured to verify compliance of a
face in an image with at least one predefined criterion, and upon
successful verification of the compliance, to send data comprising
at least a part of the image over a network; and a face identifier,
communicating with the face verifier over the network, configured
to receive the sent data and identify the face, using at least a
part of the received data.
[0024] According to a fifth aspect of the present invention there
is provided a networked system for face recognition, the system
comprising: a face verifier, configured to verify compliance of a
face in an image with at least one predefined criterion, and upon
successful verification of the compliance, to send data comprising
at least a part of the image over a network; and a face database
updater, communicating with the face verifier over the network, and
configured to receive the sent data and update a face database with
at least a part of the received data.
[0025] According to a sixth aspect of the present invention there
is provided a networked system for face recognition, the networked
system comprising: a face verifier, configured to verify compliance
of a face in an image with at least one predefined criterion, and
restrict forwarding of data comprising at least a part of the image
over a network, according to results of the verification of the
compliance.
[0026] According to a seventh aspect of the present invention there
is provided a method for face recognition, the method comprising:
a) verifying compliance of a face in an image with a predefined
criterion; b) upon the verifying of the compliance being
successful, sending data comprising at least a part of the image
over a network, for identification; and c) identifying the face in
the image, using at least a part of the sent data.
[0027] According to an eighth aspect of the present invention there
is provided a method for face recognition, the method comprising:
a) verifying compliance of a face in an image with a predefined
criterion; b) upon the verifying of the compliance being
successful, sending data comprising at least a part of the image
over a network; and c) updating a database of images with at least
a part of the sent data.
[0028] According to a ninth aspect of the present invention there
is provided a method for face recognition, the method comprising:
a) verifying compliance of a face in an image with a predefined
criterion; and b) controlling forwarding of data comprising at
least a part of the image through a network, according to a result
of the verifying of the compliance.
[0029] Unless otherwise defined, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art to which this invention belongs. The
materials, methods, and examples provided herein are illustrative
only and not intended to be limiting.
[0030] Implementation of the method and system of the present
invention involves performing or completing certain selected tasks
or steps manually, automatically, or a combination thereof.
[0031] Moreover, according to actual instrumentation and equipment
of preferred embodiments of the method and system of the present
invention, several selected steps could be implemented by hardware
or by software on any operating system of any firmware or a
combination thereof. For example, as hardware, selected steps of
the invention could be implemented as a chip or a circuit. As
software, selected steps of the invention could be implemented as a
plurality of software instructions being executed by a computer
using any suitable operating system. In any case, selected steps of
the method and system of the invention could be described as being
performed by a data processor, such as a computing platform for
executing a plurality of instructions.
BRIEF DESCRIPTION OF THE DRAWINGS
[0032] The invention is herein described, by way of example only,
with reference to the accompanying drawings. With specific
reference now to the drawings in detail, it is stressed that the
particulars shown are by way of example and for purposes of
illustrative discussion of the preferred embodiments of the present
invention only, and are presented in order to provide what is
believed to be the most useful and readily understood description
of the principles and conceptual aspects of the invention.
[0033] The description taken with the drawings making apparent to
those skilled in the art how the several forms of the invention may
be embodied in practice.
[0034] In the drawings:
[0035] FIG. 1 is a block diagram illustrating a first networked
system for face recognition, according to an exemplary embodiment
of the present invention.
[0036] FIG. 2 is a block diagram illustrating a second networked
system for face recognition, according to an exemplary embodiment
of the present invention.
[0037] FIG. 3 is a block diagram illustrating a third networked
system for face recognition, according to an exemplary embodiment
of the present invention.
[0038] FIG. 4 is a block diagram illustrating a fourth networked
system for face recognition, according to an exemplary embodiment
of the present invention.
[0039] FIG. 5 is a block diagram illustrating a fifth networked
system for face recognition, according to an exemplary embodiment
of the present invention.
[0040] FIG. 6 is a block diagram illustrating a sixth networked
system for face recognition, according to an exemplary embodiment
of the present invention.
[0041] FIG. 7 is a block diagram illustrating a seventh networked
system for face recognition, according to an exemplary embodiment
of the present invention.
[0042] FIG. 8 is a flowchart illustrating a first method for face
recognition, according to an exemplary embodiment of the present
invention.
[0043] FIG. 9 is a flowchart illustrating a second method for face
recognition, according to an exemplary embodiment of the present
invention.
[0044] FIG. 10 is a flowchart illustrating a third method for face
recognition, according to an exemplary embodiment of the present
invention.
[0045] FIG. 11 is a flowchart illustrating a fourth method for face
recognition, according to an exemplary embodiment of the present
invention.
[0046] FIG. 12 is a flowchart illustrating a fifth method for face
recognition, according to an exemplary embodiment of the present
invention.
[0047] FIG. 13 is a flowchart illustrating a sixth method for face
recognition, according to an exemplary embodiment of the present
invention.
[0048] FIG. 14 is a flowchart illustrating a seventh method for
face recognition, according to an exemplary embodiment of the
present invention.
[0049] FIG. 15 is a flowchart illustrating an eighth method for
face recognition, according to an exemplary embodiment of the
present invention.
[0050] FIG. 16 illustrates cropping of an image of a face,
according to an exemplary embodiment of the present invention.
[0051] FIGS. 17a, 17b, and 17c illustrate a face recognition
scenario, according to an exemplary embodiment of the present
invention.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0052] The present embodiments comprise a networked system and
method for recognizing a face in one or more images (say a still
image, a sequence of video images, etc.). The system may be
implemented on a wide area network such as the Word Wide Web (i.e.
the internet), on an intranet network, etc., as described in
further detail herein below.
[0053] According to an exemplary embodiment of the present
invention, a database of faces of known individuals (say criminals
or authorized users of a classified information system) is used to
store images of the faces of the known individuals, or feature
extracted from the image (say a biometric stamp), as described in
further detail hereinbelow.
[0054] In one example, a face of a user of computer station remote
from the database of faces is captured in an image (say by a still
video camera). A client module installed on the remote station,
selectively sends the image (or data which includes one or more
features extracted from the image) over the internet, for storage
on the remote face database.
[0055] The face as captured in the image has to comply with a
criterion defined in advance, before the captured image is
forwarded for storage on the database, as described in further
detail hereinbelow.
[0056] Optionally, the criterion pertains to a statistical model
run over previously received images. For example, the criterion may
be based on degree of deviation of the captured image (and thus the
face in the image) from an average image, as known in the art. The
average image is calculated from the previously received images,
using known in the art methods. In the average image, each pixel's
intensity equals an average of intensities of pixels in the same
position in the previously received images.
[0057] Optionally, the criterion is based on a comparison made
between the image and one or more images previously captured from
the same user. That is to say that the face of the user in the
captured image is compared with the face of the same user, as
captured in previously received image(s), or with an average image
calculated from previously received images of the same user, which
thus bears an average of the face, as described in further detail
hereinabove.
[0058] Optionally, the criterion is a symmetry criterion. For
example, the face as captured in the image may have to be
successfully verified as symmetric before the image (or data) is
sent, as described in further detail hereinbelow.
[0059] Optionally, the symmetry criterion is based on symmetry of a
polygon, which connects selected parts of the captured image.
[0060] Optionally, the selected parts are known elements of a human
face (say nose, eyes, or mouth). The known elements may be
identified in the captured image using known in art techniques,
such as: Viola-Jones algorithms, Neural Network methods, etc., as
known in the art. The centers of the known face elements identified
in the captured image are connected to form a polygon, and a
verified symmetry of the polygon serves as an indication for the
symmetry of the face in the captured image.
[0061] For example, the centers of the right eye, left eye, and
nose, in the captured image, may be connected to form a triangle,
which is expected to be isosceles, and thus symmetric. A successful
verification of the triangle as isosceles (say by a comparison made
between the triangle's arms) indicates that the face captured in
the image is indeed symmetric. Similarly, the centers of the eyes
and edges of lips in the captured image may be connected to form a
trapezoid, which is expected to be symmetric, etc.
[0062] Optionally, the selected parts are segments of the face in
the image. The segments are identified in the captured image, using
known in the art image segmentation methods, such as Feature
Oriented Flood Fill, Texture Analysis, Principal Component Analysis
(PCA) based methods, DFT (Discrete Fourier Transform) methods (i.e.
harmonic methods), etc., as known in the art.
[0063] The mass centers of the selected segments (say segments
positioned in parts of the image expected to include known parts of
the face, say nose, lips, or mouth) in the captured image are
connected to form a polygon. A verified symmetry of the polygon
serves as an indication for the symmetry of the face, as described
in further detail hereinabove.
[0064] Optionally, the symmetry criterion is applied on a map
representation of the image. The map representation may include,
but is not limited to: an intensity map, a phase map, a texture map
(i.e. gradient map), or any other map generated from the image
using standard image processing filters, as known in the art.
[0065] The symmetry criterion may be defined before the images are
stored in the face database, as described in further detail
hereinbelow.
[0066] Optionally, the symmetry criterion is formulated as a
threshold value for symmetry, as known in the art. The threshold
value may be a theoretical value based on theoretical calculations,
an empirical value derived from experimental data, etc., as known
in the art.
[0067] When a face in a new image (say a face of an individual who
uses the computer station and wishes to be granted access to a
classified information system) needs to be identified, the face in
the new image is tested with respect to the criterion, say the
symmetry of the face, as described in further detail hereinabove.
That is to say that the face has to comply with the criterion
before an attempt is made at identifying the face, say by
attempting to match between the captured image and images in the
remote database of faces.
[0068] Thus, according to exemplary embodiments of the present
invention, a predefined criterion is enforced on all faces
identified in images, using the methods and systems taught
hereinbelow.
[0069] The predefined criterion may improve accuracy and efficiency
of identification of the face in the image.
[0070] For example, in order to meet the symmetry criterion, an
individual may be asked to have his face aligned into a position
where the face appears symmetric (say a position where the
individual looks straight into a camera), as described in further
detail hereinbelow.
[0071] Consequently, there is produced a significantly uniform face
alignment amongst the images.
[0072] The uniform face alignment may ease identification of a face
in a new image, through comparison with images in the face
database. The identification may be eased, since the uniform face
alignment may increase similarity between face images of the same
individual, especially as far as two dimensional (2D) images are
concerned.
[0073] Consequently, false face recognition rates, such as FAR
(False Acceptance Rate) and FRR (False Rejection Rate), may be
significantly reduced.
[0074] Further, when an individual has to align his face into the
position where the individual's face appears symmetric, the
individual is less likely to use extreme facial expressions.
Extreme facial expressions (such as a widely opened mouth) are
known to posses a problem, as far as face recognition (i.e.
identification) is concerned.
[0075] The principles and operation of a system and method
according to the present invention may be better understood with
reference to the drawings and accompanying description.
[0076] 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 the
arrangement of the components as set forth in the following
description or illustrated in the drawings. The invention is
further capable of other embodiments or of being practiced or
carried out in other ways. Also, it is to be understood that the
phraseology and terminology employed herein is for the purpose of
description only, and should not be regarded as limiting.
[0077] Reference is now made to FIG. 1, which is a block diagram
illustrating a first networked system for face recognition,
according to an exemplary embodiment of the present invention.
[0078] The first networked system for face recognition includes a
face verifier 110.
[0079] Optionally, the face verifier 110 is implemented on a client
computer, say on a computer associated with an ATM (Automatic
Teller Machine) or on an end station of a Passenger Authentication
System, as described in further detail hereinbelow
[0080] The face verifier 110 verifies compliance of a face in one
or more image(s), (say a still video image of a face of an
individual, a sequence of video images of an individual, etc.),
with a predefined criterion. The predefined criterion may pertain
to a statistical model run over previously received images, a
comparison made between the image and one or more images previously
captured from the same user, symmetry, etc., as described in
further detail hereinabove.
[0081] In one example, the criterion may be a symmetry criterion
defined by a user of the first system, say using a Graphical user
Interface (GUI) implemented as a part of the face verifier 110, as
known in the art.
[0082] Optionally, the face verifier 110 uses an intensity map, for
verifying the symmetry of the face in the image, as described in
further detail hereinbelow.
[0083] Optionally, the face verifier 110 uses a texture map (i.e.
gradient map), for verifying the symmetry of the face in the image,
as described in further detail hereinbelow.
[0084] Optionally, the face verifier 110 uses a Fast Fourier
Transform (FFT) phase map, for verifying the symmetry of the face
in the image, as described in further detail hereinbelow.
[0085] Optionally, the face is a face of an individual who is a
collaborating user.
[0086] For example, the face may belong to a user who may be asked
to move into a better position. The user collaborates by moving
into a better aligned position (say a position where the user looks
directly into a still camera). A new image of the user's face, as
captured from the better aligned position, may be more symmetric,
as described in further detail hereinbelow.
[0087] Optionally, the images are a part of a video sequence, and
the video sequence is continuously fed to the face verifier 110,
say from a surveillance system (such as a video camera which
continuously captures images of a secure area), as described in
further detail hereinbelow. The face verifier 110 verifies the
symmetry of face in one or more of the images.
[0088] The first networked system for face recognition further
includes a feature extractor 112, in communication with the face
verifier 110.
[0089] Upon successfully verifying that the face complies with the
predefined criterion, say the symmetry criterion, the face verifier
110 forwards the image to the feature extractor 112.
[0090] Optionally, the face verifier 110 measures compliance of the
face with the predefined criterion in each image of a video
sequence fed to the face verifier 110. Then, the face verifier 110
selects the one or more image(s) of the face amongst the input
images, such that the measured compliance of the selected images of
the face is highest amongst the input images. The selected images
are forwarded to the feature extractor 112.
[0091] The feature extractor 112 extracts one or more features from
the image.
[0092] Optionally, the extracted features are based on parts of the
face which are known to be most invariant under changes of
illumination, noise, pose, aging, etc., as known in the art.
[0093] Optionally, the extracted features may be biometric stamps,
as known in art. For example, the feature extractor 112 may use PCA
(Principle Component Analysis) Projections, in order to generate a
vector which is used as a biometric stamp of the image (i.e. a
feature of the image), as known in art.
[0094] The feature extractor 112 may use one or more feature
extraction methods currently known in the art.
[0095] The feature extraction methods used by the feature extractor
112 may include, but are not limited to: PCA (Principal Component
Analysis), ICA (Independent Component Analysis), LDA (Linear
Discriminating Analysis), EP (Evolutionary Pursuit), EBGM (Elastic
Bunch Graph Matching), Kernel Methods, Trace Transformations, AAM
(Active Appearance Model), Three Dimensional Morphable Modeling,
Bayesian Frameworks, SVM (Support Vector Machines), HMM (Hidden
Markov Models), etc., as known in the art.
[0096] The first networked system for face recognition further
includes a face identifier 120.
[0097] The face identifier 120 communicates with the feature
extractor 112 over a computer network 115.
[0098] Optionally, the network 115 is a wide area network (say the
internet) or an intranet network. An intranet network is an
organization's internal or restricted access network that is
similar in functionality to the internet, but is only available to
the organization internally.
[0099] Optionally, the face identifier 120 identifies the face in
the image, by matching the feature(s) extracted from the image (say
a biometric stamp) with one or more features (say biometric stamps)
stored in the database, in advance, as described in further detail
hereinbelow.
[0100] For example, the face identifier 120 may use a database of
features previously extracted from face images of known
individuals, say known criminals. In the database, each feature (or
a group of features) is stored associated with data identifying a
specific one of the individuals. Exemplary data identifying the
individual may include, but is not limited to: name, address, phone
numbers, etc.
[0101] Reference is now made to FIG. 2, which is a block diagram
illustrating a second networked system for face recognition,
according to an exemplary embodiment of the present invention.
[0102] The second networked system for face recognition includes a
face verifier 210.
[0103] Optionally, the face verifier 210 is implemented on a client
computer, say on a computer associated with an ATM (Automatic
Teller Machine), as described in further detail hereinbelow.
[0104] The face verifier 210 verifies compliance of a face in one
or more image(s), (say a still video image of a face of an
individual, a sequence of video images of an individual, etc.),
with a predefined criterion. The predefined criterion may pertain
to a statistical model run over images previously received, a
criterion based on a comparison made between the image and one or
more images previously captured from the same user, a symmetry
criterion, etc., as described in further detail hereinabove.
[0105] In one example, the criterion may be a symmetry criterion
defined by a user of the first system, say using a Graphical user
Interface (GUI) implemented as a part of the face verifier 210, as
known in the art.
[0106] The face verifier 210 verifies symmetry of a face in one or
more image(s), (say a still video image of a face of an individual,
a sequence of video images of an individual, etc.), according to a
symmetry criterion.
[0107] The symmetry criterion may be defined by a user of the
second system, as described in further detail hereinbelow.
[0108] Optionally, the face verifier 210 uses an intensity map, for
verifying the symmetry of the face in the image, as described in
further detail hereinbelow.
[0109] Optionally, the face verifier 210 uses a texture map (i.e.
gradient map), for verifying the symmetry of the face in the image,
as described in further detail hereinbelow.
[0110] Optionally, the face verifier 210 uses a Fast Fourier
Transform (FFT) phase map, for verifying the symmetry of the face
in the image, as described in further detail hereinbelow.
[0111] Optionally, the face is a face of an individual who is a
collaborating user, as described in further detail hereinabove.
[0112] For example, the face may belong to a user who may be asked
to move into a better position. The user collaborates by moving
into a better aligned position (say a position where the user looks
directly into a still camera). A new image of the user's face, as
captured from the better aligned position, may be more symmetric,
as described in further detail hereinbelow.
[0113] Optionally, the images are a part of a video sequence, and
the video sequence is continuously fed to the face verifier 210,
say from a surveillance system, as described in further detail
hereinbelow. The face verifier 210 verifies the symmetry of the
face in the images of the video sequence.
[0114] The second networked system for face recognition further
includes a feature extractor 218.
[0115] The face verifier 210 communicates with the feature
extractor 218 over a computer network 215.
[0116] Optionally, the network 218 is a wide area network (say the
internet) or an intranet network, as described in further detail
hereinabove.
[0117] Upon successfully verifying the symmetry of the face in the
image, the face verifier 210 sends the image to the feature
extractor 218, over the network 215.
[0118] Optionally, the face verifier 210 measures symmetry of each
image of a video sequence fed to the face verifier 210. Then, the
face verifier 210 selects the one or more image(s) of the face
amongst the input images, such that the measured symmetry of the
selected images of the face is highest amongst the input images.
The face verifier 210 sends the selected images to the feature
extractor 218, over the network 215.
[0119] The feature extractor 218 extracts one or more features from
the image.
[0120] Optionally, the extracted features are based on parts of the
face which are known to be most invariant under changes of
illumination, noise, pose, aging, etc., as known in the art.
[0121] The feature extractor 218 may use one or more feature
extraction methods currently known in the art. The feature
extraction methods used by the feature extractor 218 may include,
but are not limited to: PCA (Principal Component Analysis), ICA
(Independent Component Analysis), LDA (Linear Discriminating
Analysis), EP (Evolutionary Pursuit), EBGM (Elastic Bunch Graph
Matching), Kernel Methods, Trace Transformations, AAM (Active
Appearance Model), Three Dimensional Morphable Modeling, Bayesian
Frameworks, SVM (Support Vector Machines), HMM (Hidden Markov
Models), etc., as known in the art.
[0122] The second networked system for face recognition further
includes a face identifier 220, in communication with the feature
extractor 218.
[0123] The face identifier 220 identifies the face in the image, by
matching the features extracted from the image with one or more
features stored in database, in advance.
[0124] For example, the face identifier 220 may use a database of
features previously extracted from face images of known
individuals, say known criminals. In the database, each feature (or
a group of features) is stored associated with data identifying a
specific one of the individuals. Exemplary data identifying the
individual may include, but is not limited to: name, address, phone
numbers, etc.
[0125] The face identifier 220 matches between the feature(s)
extracted from the image and feature(s) already stored in the
database, and identifies the face as belonging to the individual
whose name, address and phone numbers are associated with the
feature(s) matched.
[0126] Reference is now made to FIG. 3, which is a block diagram
illustrating a third networked system for face recognition,
according to an exemplary embodiment of the present invention.
[0127] The third networked system for face recognition includes a
face verifier 310.
[0128] Optionally, the face verifier 310 is implemented on a client
computer, say on a computer associated with an ATM (Automatic
Teller Machine), as described in further detail hereinbelow.
[0129] The face verifier 310 verifies compliance of a face in one
or more image(s), (say a still video image of a face of an
individual, a sequence of video images of an individual, etc.),
with a predefined criterion. The predefined criterion may pertain
to a statistical model run over images previously received, say by
calculating an average image, as described in further detail
hereinabove. The predefined criterion may be based on a comparison
made between the image and one or more images previously captured
from the same user, a symmetry criterion, etc., as described in
further detail hereinabove.
[0130] In one example, the criterion may be a symmetry criterion
defined by a user of the first system, say using a Graphical user
Interface (GUI) implemented as a part of the face verifier 310, as
known in the art.
[0131] The face verifier 310 verifies symmetry of a face in one or
more image(s), (say a still video image of a face of an individual,
a sequence of video images of an individual, etc.), according to a
symmetry criterion.
[0132] The symmetry criterion may be defined by a user of the
fourth system, as described in further detail hereinbelow.
[0133] Optionally, the face verifier 310 uses an intensity map, for
verifying the symmetry of the face in the image, as described in
further detail hereinbelow.
[0134] Optionally, the face verifier 310 uses a texture map (i.e.
gradient map), for verifying the symmetry of the face in the image,
as described in further detail hereinbelow.
[0135] Optionally, the face verifier 310 uses a Fast Fourier
Transform (FFT) phase map, for verifying the symmetry of the face
in the image, as described in further detail hereinbelow.
[0136] Optionally, the face is a face of an individual who is a
collaborating user, as described in further detail hereinabove.
[0137] For example, the face may belong to a user who may be asked
to move into a better position. The user collaborates by moving
into a better aligned position (say a position where the user looks
directly into a still camera). A new image of the user's face, as
captured from the better aligned position, may be more symmetric,
as described in further detail hereinbelow.
[0138] Optionally, the images are a part of a video sequence, and
the video sequence is continuously fed to the face verifier 310,
say from a surveillance system, as described in further detail
hereinbelow. The face verifier 310 verifies the symmetry of face in
one or more of the images.
[0139] The third networked system for face recognition further
includes a feature extractor 317.
[0140] The face verifier 310 communicates with the feature
extractor 317 over a first computer network 315.
[0141] Optionally, the first network 315 is a wide area network
(say the internet) or an intranet network, as described in further
detail hereinabove.
[0142] Upon successfully verifying the symmetry of the face in the
image, the face verifier 310 sends the image to the feature
extractor 317, over the first network 315.
[0143] Optionally, the face verifier 310 measures symmetry of each
image of a video sequence fed to the face verifier 310. Then, the
face verifier 310 selects the one or more image(s) of the face
amongst the input images, such that the measured symmetry of the
selected images of the face is highest amongst the input images.
The face verifier 310 sends the selected images the feature
extractor 317, over the first network 315.
[0144] The feature extractor 317 extracts one or more features from
the image.
[0145] Optionally, the extracted features are based on parts of the
face which are known to be most invariant under changes of
illumination, noise, pose, aging, etc., as known in the art.
[0146] The feature extractor 317 may use one or more feature
extraction methods currently known in the art. The feature
extraction methods used by the feature extractor 317 may include,
but are not limited to: PCA (Principal Component Analysis), ICA
(Independent Component Analysis), LDA (Linear Discriminating
Analysis), EP (Evolutionary Pursuit), EBGM (Elastic Bunch Graph
Matching), Kernel Methods, Trace Transformations, AAM (Active
Appearance Model), Three Dimensional Morphable Modeling, Bayesian
Frameworks, SVM (Support Vector Machines), HMM (Hidden Markov
Models), etc., as known in the art.
[0147] The third networked system for face recognition further
includes a face identifier 320.
[0148] Optionally, the face identifier 320 communicates with the
feature extractor 317 over a second computer network 319.
[0149] Optionally, the second network 319 may be the same network
as the first network 315 (that is to say that the face verifier
310, the feature extractor 317, and the face identifier 320, are
all connected by the same network, say the internet).
[0150] Optionally, the second network 319 is another network, be it
an intranet network, the internet, or another wide area network, as
described in further detail hereinabove.
[0151] The face identifier 320 identifies the face in the image, by
matching the features extracted from the image with one or more
features stored in database, in advance.
[0152] For example, the face identifier 320 may use a database of
features previously extracted from face images of known
individuals, say known criminals. In the database, each feature (or
a group of features) is stored associated with data identifying a
specific one of the individuals. Exemplary data identifying the
individual may include, but is not limited to: name, address, phone
numbers, etc.
[0153] Reference is now made to FIG. 4, which is a block diagram
illustrating a fourth networked system for face recognition,
according to an exemplary embodiment of the present invention.
[0154] The fourth networked system for face recognition includes a
face verifier 410.
[0155] Optionally, the face verifier 410 is implemented on a client
computer, say on a computer associated with an ATM (Automatic
Teller Machine), an end station at an entrance of a secure area,
etc.
[0156] Optionally, the face verifier 410 is implemented on an end
station of a Passenger Authentication System. The station is
deployed by the entrance of a plane and used for ensuring that only
a person granted a boarding pass boards the plane (and not an
impostor), etc.
[0157] The face verifier 410 verifies compliance of a face in one
or more image(s), (say a still video image of a face of an
individual, a sequence of video images of an individual, etc.),
with a predefined criterion. The predefined criterion may pertain
to a statistical model run over images previously received, a
criterion based on a comparison made between the image and one or
more images previously captured from the same user, a symmetry
criterion, etc., as described in further detail hereinabove.
[0158] In one example, the criterion may be a symmetry criterion
defined by a user of the fourth system, say using a Graphical user
Interface (GUI) implemented as a part of the face verifier 410, as
known in the art.
[0159] The face verifier 410 verifies symmetry of a face in one or
more image(s), (say a still video image of a face of an individual,
a sequence of video images of an individual, etc.), according to a
symmetry criterion.
[0160] The symmetry criterion may be defined by a user of the
fourth system, as described in further detail hereinbelow.
[0161] Optionally, the face verifier 410 uses an intensity map, for
verifying the symmetry of the face in the image, as described in
further detail hereinbelow.
[0162] Optionally, the face verifier 410 uses a texture map (i.e.
gradient map), for verifying the symmetry of the face in the image,
as described in further detail hereinbelow.
[0163] Optionally, the face verifier 410 uses a Fast Fourier
Transform (FFT) phase map, for verifying the symmetry of the face
in the image, as described in further detail.
[0164] Optionally, the face is a face of an individual who is a
collaborating user.
[0165] For example, the face may belong to a user who may be asked
to move into a better position. The user collaborates by moving
into a better aligned position (say a position where the user looks
directly into a still camera). A new image of the user's face, as
captured from the better aligned position, may be more symmetric,
as described in further detail hereinbelow.
[0166] Optionally, the images are a part of a video sequence, and
the video sequence is continuously fed to the face verifier 410,
say from a surveillance system (such as a video camera which
continuously captures images of a secure area), as described in
further detail hereinbelow.
[0167] The face verifier 410 verifies the symmetry of face in each
of the images.
[0168] Optionally, when the face verifier 410 successfully verifies
the symmetry of the face in one of the images, the face verifier
410 sends data to a remote face identifier 420, as described in
further detail hereinbelow.
[0169] Optionally, the sent data includes the whole image.
[0170] Alternatively, the sent data includes only a part of the
image. For example, the face verifier 410 may extract one or more
features from the image, using known in the art feature extraction
methods, as described in further detail hereinabove. Following the
extraction, the data which includes the features extracted from the
image is sent to a remote face identifier 420, as described in
further detail hereinabove.
[0171] Optionally, the face verifier 410 measures symmetry of each
one of two or more images of the video sequence fed to the face
verifier 410.
[0172] Then, the face verifier 410 selects one or more image(s) of
the face amongst the input images, such that the measured symmetry
of the selected images of the face is highest amongst the input
images. Consequently, data which includes at least a part of each
of the selected images is sent to a remote face identifier 420,
over a network, as described in further detail hereinbelow.
[0173] The fourth system further includes a remote face identifier
420.
[0174] The face verifier 410 communicates with the face identifier
420 over a computer network 415.
[0175] Optionally, the network 415 is a wide area network (say the
internet) or an intranet network.
[0176] The face identifier 420 identifies the face. The face
identifier 420 may use any of currently used face identification
methods, for identifying the face, as described in further detail
hereinabove.
[0177] For example, the face identifier 420 may receive data, which
includes the whole image (or a part from the image), from the
410.
[0178] The face identifier 420 may extract one or more features
from the image (or from the part of the image).
[0179] Optionally, the face identifier 420 identifies the face in
the image sent by the face verifier 410, by matching the extracted
features with feature data stored in a face database 450, in
advance of the matching.
[0180] The feature data is stored in the face database 450,
together with personal dataidentifying individuals. Upon successful
matching of the features extracted from the received data and
feature data stored in the face database 450, the face identifier
420 identifies the face in the image sent by the face verifier 410,
as belonging to an individual having the personal data associated
with the matched feature data.
[0181] Optionally, the fourth system further includes an image
capturer, connected to the face verifier 410. The image capturer
may include, but is not limited to a digital still camera, a video
camera, a web camera, etc.
[0182] The image capturer captures the image(s) of the face, and
forwards the captured image(s) to the face verifier 410.
[0183] Optionally, when the face verifier 410 finds the face in the
image non-symmetric (say when the face fails to meet the symmetry
criterion), the face verifier 410 instructs the image capturer (say
the digital still camera) to capture a new image of the face.
[0184] Optionally, upon finding the face non-symmetric, the face
verifier 410 presents an appropriate message (say a message asking
an individual whose face image is captured to look straight into
the image capturer, etc.), and the face capturer captures a new
image of the face, as described in further detail hereinbelow.
[0185] Optionally, the fourth system further includes a face
detector, in communication with the face verifier 410.
[0186] The face detector detects the face in the image. The face
detector may use one or more known in the art methods for detecting
the face in the image, including, but not limited to: a skin
detection method, a Viola-Jones detection method, a Gabor Filter
based method, etc., as described in further detail hereinbelow.
[0187] Optionally, the fourth system further includes an image
cropper, connected to the face verifier 410.
[0188] The image cropper crops the image, and thereby significantly
removes background from the image.
[0189] Optionally, the image cropper crops the image around the
face, leaving a purely facial image (i.e. an image which includes
only the face, without background details).
[0190] Optionally, the image cropper crops the image, along a
rectangle, as illustrated using FIG. 16, and described in further
detail hereinbelow.
[0191] Optionally, the fourth system also includes an image
resizer, in communication with the face verifier 410.
[0192] The image resizer resizes the image into a predefined size,
and thereby standardizes the image's size according to a size
standard predefined by a user of the fourth system, as described in
further detail hereinbelow. The size standard may improve accuracy
and efficiency of a face identifier 420, as described in further
detail hereinbelow.
[0193] Optionally, the fourth system further includes an image
illumination quality improver, in communication with the face
verifier 410.
[0194] The image illumination quality improver may improve one (or
more) qualities of illumination of the image, say using Histogram
Equalization, as known in the art and described in further detail
hereinbelow.
[0195] Reference is now made to FIG. 5, which is a block diagram
illustrating a fifth networked system for face recognition,
according to an exemplary embodiment of the present invention.
[0196] A fifth networked system for face recognition includes a
face verifier 510.
[0197] Optionally, the face verifier 510 is implemented on a client
computer, say on a computer associated with an ATM (Automatic
Teller Machine) or on an end station of a Passenger Authentication
System, as described in further detail hereinabove.
[0198] The face verifier 510 verifies compliance of a face in one
or more image(s), (say a still video image of a face of an
individual, a sequence of video images of an individual, etc.),
with a predefined criterion. The predefined criterion may pertain
to a statistical model run over images previously received, a
criterion based on a comparison made between the image and one or
more images previously captured from the same user, a symmetry
criterion, etc., as described in further detail hereinabove.
[0199] In one example, the criterion may be a symmetry criterion
defined by a user of the fifth system, say using a Graphical user
Interface (GUI) implemented as a part of the face verifier 510, as
known in the art.
[0200] The face verifier 510 verifies symmetry of a face in one or
more image(s), say a sequence of video images of an individual,
according to the symmetry criterion.
[0201] The symmetry criterion may be based on an intensity map, a
phase map, a texture map, etc., as described in further detail
hereinbelow.
[0202] Optionally, the face verifier 510 uses an intensity map, a
gradient map, a Fast Fourier Transform (FFT) phase map, or a
combination thereof, for verifying the symmetry of the face in the
image(s), as described in further detail hereinbelow.
[0203] Optionally, the face verifier 510 measures symmetry of each
one of two or more input images (say images which are a part of a
sequence of video images, or a video stream). Then, the face
verifier 510 selects the one or more image(s) of the face amongst
the input images, such that the measured symmetry of the selected
images of the face is highest amongst the input images.
[0204] The face verifier 510 may further receive data identifying
the face from a user, say using a user interface implemented as a
part of the face verifier 510, or a user interface in association
therewith, as known in the art. The user may be an operator of the
fifth system, the person whose face is captured in the image(s),
etc. The data identifying face may include, but is not limited to
details such as a passport number, a name, or an address.
[0205] The fifth system further includes a face database updater
530.
[0206] The face verifier 510 communicates with the face database
updater 530 over a computer network 515.
[0207] Optionally, the network 515 is a wide area network (say the
internet) or an intranet network. For example, the intranet network
515 may connect computers and ATMs (Automatic Teller Machines) in
one or more branches and offices of a commercial bank.
[0208] When the face verifier 510 successfully verifies the
symmetry of the face in one of the images (say the face of a
criminal), the face verifier 510 sends data, over the network 515,
to the face database updater 530. The sent data may include the
whole image, or a part of the image, say one or more features
extracted from the image, such as biometric stamps, as described in
further detail hereinabove.
[0209] Optionally, the sent data further includes data identifying
the face, as described in further detail hereinbelow.
[0210] The face database updater 530 updates a face database 550
with the received data, or a part thereof, associated with the data
identifying the face, as described in further detail
hereinbelow.
[0211] Optionally, the received data includes only one or more
features extracted from the face (i.e. a part of the image).
[0212] Alternatively, the received data includes the whole image
(or a significant part thereof). The face database updater 530
extracts one or more features from the image (or from the
significant part), and stores the extracted features in the face
database 550.
[0213] The face database 550 may be a local database, a remote
database accessible through the Internet, etc., as known in the
art.
[0214] Reference is now made to FIG. 6, which is a block diagram
illustrating a sixth networked system for face recognition,
according to an exemplary embodiment of the present invention.
[0215] A sixth networked system for face recognition includes a
face verifier 610.
[0216] The face verifier 610 verifies compliance of a face in one
or more image(s), (say a still video image of a face of an
individual, a sequence of video images of an individual, etc.),
with a predefined criterion. The predefined criterion may pertain
to a statistical model run over images previously received, a
criterion based on a comparison made between the image and one or
more images previously captured from the same user, a symmetry
criterion, etc., as described in further detail hereinabove.
[0217] In one example, the criterion may be a symmetry criterion
defined by a user of the sixth system, as described in further
detail hereinabove.
[0218] The face verifier 610 receives one or more first image(s) of
a face, together with data identifying the face, as described in
further detail hereinabove.
[0219] The data identifying the face may include, but is not
limited to details such as a passport number, a name, or an
address. The details may be provided by an operator of the sixth
system, by an individual whose face is captured in the image,
etc.
[0220] The face verifier 610 verifies symmetry of a face in one or
more of the first image(s), according to a symmetry criterion.
[0221] For example, the first image(s) may include a still video
image of a face of an individual who enrolls in a security system,
or a sequence of video images of a known criminal the police wishes
to store in a database of criminal suspects.
[0222] The symmetry criterion may be defined by a user of the sixth
system, say using a Graphical User Interface (GUI), as described in
further detail hereinbelow. The symmetry criterion may be based on
an intensity map, a phase map, a texture map, etc., as described in
further detail hereinbelow.
[0223] Optionally, the face verifier 610 uses an intensity map, a
gradient map, a Fast Fourier Transform (FFT) phase map, or a
combination thereof, for verifying the symmetry of the face in the
first image(s), as described in further detail hereinbelow.
[0224] Optionally, the face verifier 610 measures symmetry of each
one of two or more images input to the face verifier 610, say
images which are a part of a sequence of video images streamed to
the face verifier 610. Then, the face verifier 610 selects one or
more first image(s) of the face amongst the input images, such that
the measured symmetry of the selected image(s) of the face is
highest amongst the input image(s).
[0225] The sixth system further includes a face database updater
630,
[0226] The face verifier 610 communicates with the face database
updater 630 over a computer network 615.
[0227] Optionally, the network 615 is a wide area network (say the
internet) or an intranet network, as described in further detail
hereinabove.
[0228] When the face verifier 610 successfully verifies the
symmetry of the face in one (or more) of the first images (say the
face of a criminal), the face verifier 610 sends data over the
network 615, including data identifying the face.
[0229] Optionally, the sent data includes the whole of the first
image(s).
[0230] Alternatively, the sent data includes only a part of each of
the first image(s).
[0231] For example, the data may include one or more features
extracted from each of the first image(s), by the face verifier
610, say a biometric stamp extracted from the first image, as
described in further detail hereinabove.
[0232] The face database updater 630 updates a face database 650
with the received data or with features extracted from the received
data, as described in further detail hereinbelow.
[0233] Optionally, the face database updater 630 updates the face
database 650 with the images selected by the face verifier 610 or
with data extracted from the selected images, as described in
further detail hereinabove.
[0234] The sixth system further includes a face identifier 620.
[0235] The face verifier 610 communicates with the face identifier
620 over the computer network 615, say over the intranet, as
described in further detail hereinabove.
[0236] When one or more second image(s) of a face are presented to
the face verifier 610 (say, a video stream of an individual who
attempts to walk into a secure area), the face verifier 610
verifies the symmetry of the face in the second image, according to
the predefined symmetry criterion.
[0237] If the symmetry of the face in one or more of the second
images is successfully verified, the face verifier 610 sends data
which includes at least a part of the second image, over the
network 615, as described in further detail hereinabove.
[0238] The face identifier 620 receives the data sent by face
verifier 610. The face identifier 620 identifies the face in the
second images, say using the face database 650, and the received
data, as described in further detail hereinabove.
[0239] In one example, an authorized user of classified information
system enrolls in the classified information system.
[0240] A first image of the authorized user's face is input to the
face verifier 610 (say a passport photo), together with data
identifying the authorized user, say using a Graphical User
Interface (GUI). The data identifying the user may include, but is
not limited to details such as a passport number, a name, an
address, a role, etc. The details may be provided by an operator of
the sixth system, by the authorized user himself, etc.
[0241] If the face verifier 610 verifies the symmetry of the
authorized user's face in the first image, the face verifier 610
sends data which includes the first image (or a part of the first
image) to the database updater 630, over the network 615, together
with the data identifying the authorized user. The database updater
630 updates the face database 650 with the received data.
[0242] The next time the authorized user wishes to log into the
classified information system, a second image of his face is
captured live, say by a still camera in communication with the
classified information system.
[0243] The face verifier 610 receives the second image and verifies
the symmetry of the authorized user's face in the second image.
[0244] As the symmetry of the authorized user's face in the second
image is successfully verified, the face verifier 610 sends data,
which includes the second image (or a part of the second image)
over the network 615. The face identifier 620 receives the sent
data and identifies the face in the second image, using the face
database 650, as described in further detail hereinbelow.
[0245] Consequently, upon positive identification of the authorized
user's face, the authorized user is allowed to log into the
classified information system.
[0246] Reference is now made to FIG. 7, which is a block diagram
illustrating a seventh networked system for face recognition,
according to an exemplary embodiment of the present invention.
[0247] A seventh networked system for face recognition includes a
face verifier 710.
[0248] The face verifier 710 verifies compliance of a face in one
or more image(s), with a predefined criterion. The predefined
criterion may pertain to a statistical model run over images
previously received, a comparison made between the image and one or
more images previously captured from the same user, symmetry, etc.,
as described in further detail hereinabove.
[0249] In one example, the criterion may be a symmetry criterion
defined by a user of the seventh system, say using a Graphical user
Interface (GUI) implemented as a part of the face verifier 710, as
known in the art.
[0250] The face verifier 710 verifies symmetry of a face in one or
more image(s) received by the face verifier 710.
[0251] The images may include, but are not limited to a still video
image of a face of an individual, or a sequence of video images of
an individual.
[0252] The face verifier 710 verifies the symmetry according to a
symmetry criterion.
[0253] The symmetry criterion may be based on an intensity map, a
phase map, a texture map, etc., as described in further detail
hereinbelow.
[0254] Optionally, the face verifier 710 uses an intensity map, a
gradient map, a fast Fourier Transform (FFT) phase map, an image
processing filter output (as known in art), or a combination
thereof, for verifying the symmetry of the face in the image(s), as
described in further detail hereinbelow.
[0255] The face verifier 710 further restricts forwarding of data,
which includes at least a part of the image to a remote receiver
over a network 715 (say the internet), according to results of the
verification of the symmetry by the face verifier 710.
[0256] For example, the face verifier 710 may restrict the
forwarding of images to a party, who offers face identification
services over the internet.
[0257] In a first example, the face verifier 710 finds the face in
the image to be non-symmetric (i.e. the face fails to meet the
symmetry criterion). Consequently, the face verifier 710 blocks the
forwarding of data which includes the image (or a part thereof) to
the image identifier 420 described hereinabove (using FIG. 4),
through a network 715 (say the internet), as described in further
detail hereinabove.
[0258] Optionally, the face verifier 710 also presents an
appropriate message.
[0259] For example, the face verifier 710 may present a message
asking an individual whose face image is captured to look straight
into an image capturer (say, a still camera), or to align in a
position in front of the image capturer, as described in further
detail hereinbelow.
[0260] Then, the image capturer may capture a new (and hopefully,
symmetric) image of the face of the individual.
[0261] When the face verifier 710 finds that the face successfully
meets the symmetry criterion (and is thus successfully verified),
the face verifier 710 forwards data which includes at least a part
of the image to the image identifier 420, through the network 715,
as described in further detail hereinabove.
[0262] In a second example, the face verifier 710 may forward the
data to one or more destination(s) set in advance of the
verification of the symmetry, say by an operator of the seventh
system. The destination(s) may include, but are not limited to: an
email address, a database server of a third party, or an
application (which may run on a remote computer, etc.).
[0263] The seventh system may be used as a stand alone product, or
in combination with other systems, say a face recognition system, a
security system, etc.
[0264] Reference is now made to FIG. 8, which is a flowchart
illustrating a first method for face recognition, according to an
exemplary embodiment of the present invention.
[0265] In a first method for face recognition, according to an
exemplary embodiment of the present invention, there is verified
810 the compliance of a face with a predefined criterion, as
described in further detail hereinabove.
[0266] For example, the compliance of a face in one or more
image(s), may be verified 810 using a predefined criterion. The
predefined criterion may pertain to a statistical model run over
images previously received, a criterion based on a comparison made
between the image and one or more images previously captured from
the same user, a symmetry criterion, etc., as described in further
detail hereinabove.
[0267] In one example, the criterion may be a symmetry criterion
defined by a user of the fourth system, say using a Graphical user
Interface (GUI) implemented as a part of the face verifier 410, as
described in further detail and illustrated using FIG. 4
hereinabove.
[0268] The symmetry of a face in one or more image(s), (say a still
video image of a face of an individual, a sequence of video images
of an individual, etc.) is automatically verified 810 according to
a symmetry criterion, say using the face verifier 410, as described
in further detail hereinbelow.
[0269] The symmetry criterion may be based on an intensity map, a
phase map, a texture map, an image processing filter, etc., as
described in further detail hereinbelow.
[0270] Optionally, the first method further includes using an
intensity map, for verifying 810 the symmetry of the face in the
image, as described in further detail, hereinbelow.
[0271] Optionally, the first method further includes using a
gradient map, for verifying 810 the symmetry of the face in the
image, as described in further detail, hereinbelow.
[0272] Optionally, the first method further includes using a fast
Fourier Transform (FFT) phase map, for verifying 810 the symmetry
of the face in the image, as described in further detail,
hereinbelow.
[0273] Optionally, the first method further includes measuring
symmetry of each one of two or more input images (say images which
are a part of a sequence of video images, or a video stream). Then,
the one or more image(s) of the face are selected amongst the input
images, such that the measured symmetry of the selected images of
the face is highest amongst the input images.
[0274] Upon successful verification 810 of the symmetry of the face
in the image, data of the image is sent 815 through a computer
network (say the internet), for identification.
[0275] The data of the image may include the whole image, or a part
thereof (say one or more features extracted from the image, such as
a biometric stamp, as described in further detail hereinabove).
[0276] Finally, the face in the image is identified 820, using the
data, or features extracted from the data, say by the face
identifier 420, as described in further detail hereinabove.
[0277] Optionally, the first method further includes a preliminary
step of capturing the image of the face, and forwarding the
captured image, for the symmetry verification, (say to the face
verifier 410), as described in further detail hereinabove.
[0278] Optionally, the first method further includes detecting the
face in the image, say using the face detector, as described in
further detail hereinbelow.
[0279] The detection of the face may be carried out using one or
more methods, as known in the art, including, but not limited to: a
skin detection method, a Viola-Jones detection method, a Gabor
Filter based method, etc., as described in further detail
hereinbelow.
[0280] Optionally, the first method further includes cropping the
image.
[0281] Optionally, the cropping may be carried out around the face,
thereby leaving a purely facial image (i.e. substantially without
background).
[0282] Optionally, the cropping may be carried out along a
rectangle, significantly removing background from the image, as
illustrated using FIG. 16, and described in further detail
hereinbelow.
[0283] Optionally, the first method further includes resizing the
image into a predefined size, and thereby standardizing the image's
size according to a predefined size standard, as described in
further detail hereinbelow.
[0284] Optionally, the first method further includes improving one
or more qualities of illumination of the image, say using Histogram
Equalization methods, as described in further detail
hereinbelow.
[0285] Reference is now made to FIG. 9, which is a flowchart
illustrating a second method for face recognition, according to an
exemplary embodiment of the present invention.
[0286] In a second method for face recognition, according to an
exemplary embodiment of the present invention, there is verified
910 the compliance of a face with a predefined criterion, as
described in further detail hereinabove.
[0287] For example, the compliance of a face in one or more
image(s) may be verified 910 with a predefined criterion. The
predefined criterion may pertain to a statistical model run over
images previously received, a criterion based on a comparison made
between the image and one or more images previously captured from
the same user, a symmetry criterion, etc., as described in further
detail hereinabove.
[0288] In one example, symmetry of a face in one or more image(s),
(say a still video image of a face of an individual, a sequence of
video images of an individual, etc.) is verified 910 according to a
symmetry criterion, say using the face verifier 510, as described
in further detail hereinbelow, and illustrated using.
[0289] The symmetry criterion may be defined by a user of the fifth
system described in further detail hereinbelow.
[0290] Optionally, the second method further includes using an
intensity map, for verifying 910 the symmetry of the face in the
image, as described in further detail, hereinbelow.
[0291] Optionally, the second method further includes using a
gradient map, for verifying 910 the symmetry of the face in the
image, as described in further detail, hereinbelow.
[0292] Optionally, the second method further includes using a fast
Fourier Transform (FFT) phase map, for verifying 910 the symmetry
of the face in the image, as described in further detail,
hereinbelow.
[0293] Optionally, the second method further includes measuring
symmetry of each one of two or more input images (say images which
are a part of a sequence of video images, or a video stream). Then,
one or more image(s) of the face are selected amongst the input
images, such that the measured symmetry of the selected images of
the face is highest amongst the input images.
[0294] If the symmetry of the face in the image(s) is successfully
verified 910 (say by the face verifier 510), data of the selected
image(s) is sent 915 to a face database updater 530 in
communication with the face verifier 510. The data of the selected
images is sent 915 over the computer network 515, say over the
internet or an intranet network, as described in further detail
hereinabove.
[0295] The sent data may include the whole image, or a part thereof
(say one or more features extracted from the image, as described in
further detail hereinabove).
[0296] Optionally, a face database 550 is updated 930 with the data
of the selected image(s) and associated data identifying the face,
say by the face database updater 530, as described in further
detail hereinabove. Alternatively, the face database 550 is updated
with one or more features extracted from the received data, as
described in further detail hereinabove.
[0297] The data identifying face may include, but is not limited to
details such as a passport number, a name, or an address. The
details may be provided by an operator of the second system
described hereinabove, by an individual whose face is captured in
the image, etc.
[0298] Reference is now made to FIG. 10, which is a flowchart
illustrating a third method for face recognition, according to an
exemplary embodiment of the present invention.
[0299] In a third method, according to an exemplary embodiment of
the present invention, there is verified 1010 the compliance of a
face in a first image with a predefined criterion, as described in
further detail hereinabove.
[0300] For example, the compliance of a face in one or more first
image(s), may be verified 1010 with a criterion which pertains to a
statistical model run over images previously received, a criterion
based on a comparison made between the image and one or more images
previously captured from the same user, a symmetry criterion, etc.,
as described in further detail hereinabove.
[0301] In one example, symmetry of a face in one or more first
image(s), (say a still video image of a face of an individual, a
sequence of video images of an individual, etc.) is verified 1010
according to a symmetry criterion, say using the face verifier 610,
as described in further detail hereinbelow.
[0302] The symmetry criterion may be defined by a user of the sixth
system described in further detail hereinbelow.
[0303] For example, symmetry of a face in one or more first
image(s) is verified 1010, according to a symmetry criterion, say
using the face verifier 610, as described in further detail
hereinbelow.
[0304] For example, the first image(s) may include a passport photo
of an individual who enrolls in a security system, a sequence of
video images of a known criminal the police wishes to store in a
database of criminal suspects, etc.
[0305] The symmetry criterion may be defined by a user of the sixth
system, as described in further detail hereinbelow.
[0306] Optionally, the verification of the symmetry of the first
image(s) is carried out using an intensity map, a texture map (i.e.
gradient map), a fast Fourier Transform (FFT) phase map, or a
combination thereof, as described in further detail
hereinbelow.
[0307] Next, the data of the first images is sent 1030 to a
database 650 in communication with the face verifier 610 over a
computer network (say the internet). The sent data may include
whole images, or a part thereof (say one or more features extracted
from the image, as described in further detail hereinabove).
[0308] Next, the database 650 is updated 1030 with the sent data
and associated data identifying the face. The data is sent 1030
(and updated), only if the symmetry of the face in the first
image(s) is successfully verified 1010, say by the face verifier
610, as described in further detail hereinabove.
[0309] The data identifying face may include, but is not limited to
details such as a passport number, a name, or an address. The
details may be provided by an operator of the sixth system
described hereinabove, by the individual whose face is captured in
the image, etc.
[0310] When one or more second image(s) of the face are presented
to the face verifier 610 (say, a video stream of a criminal who
attempts to walk into a secure area), the symmetry of the face in
the second image(s) is verified 1070, according to the predefined
symmetry criterion, as described in further detail hereinabove.
[0311] If the symmetry of the face in the second image(s) is
successfully verified 1070, data of the second image(s) is sent
1090 to the face identifier 620, and the face in the second
image(s) is identified 1090, say by the face identifier 620, using
the face database 650, as described in further detail
hereinabove.
[0312] For example, a police unit may wish to store a face image
together with identifying data of a known criminal in a suspect
database.
[0313] The symmetry of the known criminal's face in the first image
is verified 1010, say using the face verifier 610, as described in
further detail hereinbelow.
[0314] If the symmetry of the known criminal's face in the first
image is successfully verified 1010, the suspect database is
updated 1030 with the first image of the known criminal, together
with the data identifying the known criminal.
[0315] A surveillance camera may capture a video stream (i.e.
second images of the criminal) of the criminal in a crime scene.
The video stream may be used to identify the criminal, say using
one of the systems described in further detail hereinabove.
[0316] When the symmetry of the criminal's face in the second image
is successfully verified 1070, the face in the second image may be
identified 1090, using the police unit's suspect database.
[0317] Consequently, upon positive identification of the criminal's
face, the police may arrest the known criminal, and use the video
stream as evidence against the known criminal.
[0318] Reference is now made to FIG. 11, which is a flowchart
illustrating a fourth method for face recognition, according to an
exemplary embodiment of the present invention.
[0319] In a fourth method, according to an exemplary embodiment of
the present invention, there is verified 1110 the compliance of a
face with a predefined criterion, as described in further detail
hereinabove.
[0320] For example, the compliance of a face in one or more
image(s), may be verified 1110 with a criterion which pertains to a
statistical model run over images previously received, a criterion
based on a comparison made between the image and one or more images
previously captured from the same user, a symmetry criterion, etc.,
as described in further detail hereinabove.
[0321] In one example, an image of a face is captured 1100, say
using an image capturer, such as a digital still camera, a video
camera, or a surveillance camera (which constantly streams video
images of a secure area).
[0322] For example, a user may approach a face recognition system,
which includes the sixth system described in further detail
hereinabove, as well as the image capturer (say a video
camera).
[0323] Optionally, the image capturer may be triggered to capture
the image of a user who approaches the face recognition system by a
smart card reader connected to the image capturer.
[0324] Upon insertion of a smart cart into the smart card reader,
by the user, the smart card reader triggers the image capturer, to
capture the image of the user. Then, the captured image is
forwarded for symmetry verification, as described in further detail
hereinbelow.
[0325] Similarly, the image capturer may be triggered to capture
the image of the face of the user who approaches the face
recognition system, by a RFID (Radio frequency identification) card
reader connected to the image capturer. The RFID card reader
triggers the image capturer to capture the image, when the user
inserts an RFID cart into the RFID reader. Then, the captured image
is forwarded for symmetry verification, as described in further
detail hereinbelow.
[0326] Optionally, the image capturer continuously captures images.
For example, the imager capturer may be a surveillance camera,
which constantly streams video images of a secure area. Upon
detection of the user's face in the image (say by the face
detector), the image is forwarded to the face verifier 610, as
describe in further detail hereinabove.
[0327] Optionally, the image is captured in a two dimensional (2D)
format, as known in the art.
[0328] Optionally, the image is captured in a three dimensional
(3D) format, as known in the art.
[0329] Next, the face in the captured image is verified 1110,
according to a symmetry criterion, say by the face verifier 610, as
described in further detail hereinabove.
[0330] Optionally, when the face in the image is found to be
non-symmetric (i.e.
[0331] when the face fails to meet the symmetry criterion), the
image capturer is instructed (say by the face verifier 610) to
capture a new image of the face.
[0332] The image capturer may present an appropriate message, say a
message asking an individual whose face image is captured to look
straight into the image capturer, or align in a position in front
of the image capturer (say, a still camera), as described in
further detail hereinbelow. Then, the image capturer captures a new
image of the face.
[0333] When the symmetry of the face is successfully verified 1110,
data which includes the image (or a part of the image) is sent for
identification, say over a wide area network, such as the internet,
as described in further detail hereinabove.
[0334] Optionally, before identification the image is pre-processed
1180, say by the face identifier 620, using one of several
pre-processing methods currently used for face recognition. The
pre-processing methods may be used for sharpening, grey scale
modification, removal of red eyes, etc., as known in the art.
[0335] Optionally, there are extracted one or more features from
the image, as described in further detail hereinabove.
[0336] Finally, the face is identified 1190, say by the face
identifier 420, as described in further detail hereinabove.
[0337] Reference is now made to FIG. 12, which is a flowchart
illustrating a fifth method for face recognition, according to an
exemplary embodiment of the present invention.
[0338] In a fifth method, according to an exemplary embodiment of
the present invention, a video image is captured 1200, say by a
video still camera, as described in further detail hereinabove.
[0339] Next, there is detected 1201 a face in the captured
image.
[0340] The face may be detected using one or more methods currently
used for detecting a face in the image. The methods currently used
may include, but are not limited to: Viola-Jones detection methods,
Gabor Jets based methods, skin detection methods, histogram
analysis methods, or other methods (say methods based on edge maps,
gradients, or standard face shapes, etc.), as known in the
art).
[0341] Viola-Jones methods use several image processing filters
over the whole image. A neural network algorithm is trained over a
training set (say a set of already processed face images). The face
is detected using the neural network algorithm and a search is made
to find best match values that predict the face center location, as
known in the art.
[0342] Gabor Jets methods use a convolution of Fourier Coefficient
of the image with wavelets coefficients of low order, where the
values that predict face location are set according to empirically
found predictive values, as known in the art.
[0343] Skin detectors analyze an intensity map presentation of the
image, in order to find that pixel intensity values which comply
with standard skin values, as known in the art.
[0344] Histogram analysis methods analyze a histogram of the image,
say a Pixel Frequency Histogram, after applying several filters
(histogram normalization, histogram stretching, etc.) on the
histogram of the image. The filters applied on the image's
histogram may enable separation of face from background, as known
in the art.
[0345] Next, the image is cropped 1202, and thus background is
significantly removed from the image, as illustrated using FIG. 16,
and described in further detail hereinbelow.
[0346] Then, the cropped image is resized 1203 into a size, in
accordance with a side standard. The size standard may be set by an
operator of the first system described in further detail
hereinabove.
[0347] The size standard may improve accuracy and efficiency of
identification of the face, since images in a database of face
images, which are substantially the same size as the resized image,
are more likely be successfully matched with the resized image, for
identifying the face, as described in further detail
hereinabove.
[0348] Next, there are improved 1204 one or more illumination
qualities of the image.
[0349] For example, the illumination qualities of the image may be
enhanced using Histogram Equalization, which modifies the dynamic
range and contrast of an image by altering the image, as known in
the art.
[0350] Optionally, the histogram equalization employs a monotonic,
non-linear mapping, which re-assigns the intensity values of pixels
in the image, such that the improved image contains a uniform
distribution of intensities (i.e. a flat histogram).
[0351] Histogram Equalization is usually introduced using
continuous (rather than discrete) process functions, as known in
the art.
[0352] Optionally, the histogram equalization employs linear
mapping, exponential (or logarithmic) mapping, etc., as known in
the art.
[0353] Next, the symmetry of the face in the image is verified
1207, using a symmetry criterion, as described in further detail
hereinbelow.
[0354] Upon successful verification 1215 of the symmetry of the
face in the image, data which includes at least a part of the image
is sent 1220 over a network (say a wide area network, such as the
internet). The face in the image is identified 1220, say using the
face identifier 620, as described in further detail
hereinabove.
[0355] If the image is found to be non-symmetric (i.e if the image
fails to comply with the symmetry criterion), an image of the face
is captured again 1200, as described in further detail
hereinabove.
[0356] The symmetry criterion may be based on an intensity map of
the image, a phase map of the image, a texture map of the image, a
statistical model run over images previously received (say by
comparison with an average image calculated from of previously
received images, which is likely to be symmetric), a comparison
made between the image and one or more images previously captured
from the same user, etc., as described in further detail
hereinabove.
[0357] The symmetry criterion may be predefined before the images
are stored in a face database, as described in further detail
hereinabove.
[0358] In the face database, there are stored images of known
faces, which also meet the face criterion, as described in further
detail hereinbelow. Thus, according to exemplary embodiments of the
present invention, the symmetry criterion is enforced on all face
images the method is used on.
[0359] The symmetry criterion may improve accuracy and efficiency
of identification of the face in the image.
[0360] For example, in order to meet the face criterion, the face
is aligned into a position where the face appears symmetric (say a
position where an individual looks straight into a camera).
[0361] Consequently, there is produced a significantly uniform face
alignment amongst the images.
[0362] The uniform face alignment may ease identification of a face
in a new image, through comparison with images in the face
database. The identification may be eased, since the uniform face
alignment may increase similarity between face images of the same
individual, especially as far as two dimensional (2D) images are
concerned.
[0363] Consequently, face recognition rates, such as FAR (False
Acceptance Rate) and FRR (False Rejection Rate), may be
improved.
[0364] Further, when an individual has to align his face into the
position where the individual's face appears symmetric, the
individual is less likely to use extreme facial expression. Extreme
facial expressions (such as a widely opened mouth) are known to
posses a problem, as far as face recognition (i.e. identification)
is concerned.
[0365] Reference is now made to FIG. 13, which is a flowchart
illustrating a sixth method for face recognition, according to an
exemplary embodiment of the present invention.
[0366] In a sixth method for face recognition, according to an
exemplary embodiment of the present invention, there is verified
1310 the compliance of a face with a predefined criterion, as
described in further detail hereinabove.
[0367] In one example, symmetry of a face in one or more image(s),
(say a still video image of a face of an individual, a sequence of
video images of an individual, etc.) is verified 1310 according to
a symmetry criterion, say using the face verifier 710, as described
in further detail hereinbelow.
[0368] The symmetry criterion may be defined by a user of the
seventh system, as described in further detail hereinbelow.
[0369] Optionally, the sixth method further includes using an
intensity map, for verifying 1310 the symmetry of the face in the
image, as described in further detail hereinbelow.
[0370] Optionally, the sixth method further includes using a
gradient map, for verifying 1310 the symmetry of the face in the
image, as described in further detail hereinbelow.
[0371] Optionally, the sixth further includes using a fast Fourier
Transform (FFT) phase map, for verifying 1310 the symmetry of the
face in the image, as described in further detail, hereinbelow.
[0372] Next, the image's forwarding is controlled 1370 (say by the
face verifier 710, as described in further detail hereinabove).
[0373] For example, when the face in the image to is found to be
non-symmetric, the sending of data which includes the image (or a
part of the image), over the internet (or another wide area
network) may be blocked.
[0374] Reference is now made to FIG. 14, which is a flowchart
illustrating a seventh method for face recognition, according to an
exemplary embodiment of the present invention.
[0375] A seventh method, according to a preferred embodiment of the
present invention uses an intensity map of a image captured, say by
an image capturer (a still camera, a video camera, etc.)
[0376] In the seventh method, the face is found 1401 in the image,
as described in further detail hereinabove.
[0377] Next, the image is cropped 1402, say 15% in each side (top,
bottom, right and left), along a rectangle, as described in further
detail, and illustrated using FIG. 16 hereinbelow.
[0378] The cropped image is resized 1403, say to hundred on hundred
pixels.
[0379] Optionally, the image is modified, using histogram
equalization 1404 (say Linear Histogram Equalization), as described
in further detail hereinabove.
[0380] Next, there is verified the symmetry of the face in the
image, through the following:
[0381] The image is divided 1405 into equal parts: a left side and
a right side, along a vertical line passing through a point in the
middle of the image.
[0382] Next, an average pixel intensity is calculated 1406 using
all pixels of the right part, denoted hereinbelow as: Right Avg.,
and an average intensity is calculated 1406 using all pixels of the
left part, denoted hereinbelow as: Left Avg.
[0383] Next, the left side is transformed 1407. For each old pixel
P old (.sub.i,j) of of the left size, there is computed a new value
using Formula 1, yielding a corresponding new value for the pixel,
denoted hereinbelow as P.sub.new (i,j).
P new ( i , j ) = P old ( i , j ) .times. Right Avg . Left Avg .
Formula 1 ##EQU00001##
[0384] The new pixel values P.sub.new(i, j) form a new image, which
comprises the new values calculated for the pixels of the left
side, and the original values of the pixels of the right side. The
new image is denoted hereinbelow as: I.sub.new.
[0385] Next the new image I.sub.new is flipped 1408 over a central
vertical line (i.e. a line which divides the new image into two
equal parts, at the image's center), to form a flipped image
denoted hereinbelow as I.sub.flipped.
[0386] Then, for each of pixel (I, J) there is computed a
difference 1409 between intensity of the pixel in I.sub.new and the
intensity of the pixel in I.sub.flipped, using Formula 2:
Diff i,j=|I new(i, j)-I flipped(i, j)| Formula 2
[0387] The resultant difference is denoted: Diff.sub.I,j
[0388] Next, there is computed 1410 the symmetry of the face by
dividing the average of the differences (Diff.sub.I,j) of
intensities of the pixels calculated using Formula 2, by the
average of intensities of the pixels of I.sub.new, as formulated by
Formula 3:
Symmetry = Avg ( Diff i , j ) Avg ( I new ) Formula 3
##EQU00002##
[0389] According to an exemplary embodiment, the threshold for
symmetry (i.e. symmetry criterion) is set at 0.35. If
symmetry<0.35, the face is successfully verified 1411 as
symmetric. If symmetry>=0.35, the face is determined to be
non-symmetric, and a new image has to be captured, as described in
further detail hereinabove.
[0390] Reference is now made to FIG. 15, which is a flowchart
illustrating an eighth method for face recognition, according to an
exemplary embodiment of the present invention.
[0391] An eighth method, according to a preferred embodiment of the
present invention uses a phase map of an image captured (say by an
image capturer (a still camera, a video camera, etc.) The phase map
may be calculated using Fourier Transform (FT), as known in the
art.
[0392] In the eighth method, the face is found 1501 in the image,
as described in further detail hereinabove.
[0393] Next, the image is cropped 1502, say 15% in each side (top,
bottom, right and left), along a rectangle, as described in further
detail, and illustrated using FIG. 16 hereinbelow.
[0394] The cropped image is resized 1503, say to hundred on hundred
pixels.
[0395] Optionally, the image is modified, using histogram
equalization 1504 (say Linear Histogram Equalization), as described
in further detail hereinabove.
[0396] Next, there is verified the symmetry of the face in the
image, through the following:
[0397] The image is divided 1505 into equal parts: a left side and
right side, along a vertical line.
[0398] Next, the right side is flipped 1506 vertically.
[0399] Next, there is computed 1507 the Fourier Transform (FT) for
the right side and for the left side. The resultant phase maps are
denoted hereinbelow as I.sub.right and I.sub.left respectively.
[0400] Next, there is computed 1508 the difference between
I.sub.right and I.sub.left, using Formula 4. where Diff denotes the
difference between the two.
Diff=|I right-I left| Formula 4
[0401] Next, there is computed 1509 symmetry for the image, using
Formula 5.
Symmetry = Diff Number of pixels of half image Formula 5
##EQU00003##
[0402] According to an exemplary embodiment, the threshold for
symmetry (i.e. the symmetry criterion) is set at 35. If
symmetry<35, the face is successfully verified 1510 as
symmetric. If symmetry>=35, the face is determined to be
non-symmetric, and a new image has to be captured, as described in
further detail hereinabove.
[0403] Reference is now made to FIG. 16, which illustrates cropping
of an image of a face, according to an exemplary embodiment of the
present invention.
[0404] According to an exemplary embodiment of the present
invention, an image of a face may be cropped, say 15% of each size,
a long a rectangle. Consequently the background is significantly
removed from the image.
[0405] The cropping of the image may result in a more efficient and
accurate face recognition, as the identifying is carried out on the
face 1611 itself, without unnecessary processing of background
details, such as a collar 1612, which have nothing to do with the
face itself.
[0406] The removal of the background details may also ease
identification of a face, by introducing increased similarity
between face images of the same individual, especially as far as
two dimensional (2D) images are concerned.
[0407] The methods for face recognition, as described hereinabove,
may also be used in a variety of systems where symmetry information
may prove helpful.
[0408] The systems may include, but are not limited to: 2D or 3D
systems, security system, access control, HLS (Home Land Security),
ATM (Automatic Teller Machines), web portals, or any application
which requires recognition of the subject.
[0409] The systems may also include: passport picture capturing,
standard image capturing (thus enforcing a standard for image
capturing, say for e-Passport or e-ID generation, as known in the
art).
[0410] The systems described in further detail hereinabove, may be
implemented using a Personal Computer, an embedded system, a FPGA
(Field Programmable Gate Array), or any other computing device.
[0411] Reference is now made to FIGS. 17A, 17B, and 17C, which
illustrate a face recognition scenario, according to an exemplary
embodiment of the present invention.
[0412] In a first recognition scenario, according to an exemplary
embodiment of the present invention, a user approaches a face
recognition system, say a face recognition system based on the
seventh system described in further detail hereinabove.
[0413] The user may be asked to get closer to a camera (say using a
message displayed on a video monitor), as illustrated in FIG.
17A.
[0414] Next, an image of the user's face is captured by the
camera.
[0415] If the face verifier 710 finds the face in the image to be
non-symmetric, the user is asked to look straight into the camera
(say using a message displayed on a video monitor), as illustrated
in FIG. 17B.
[0416] The camera captures a second image of the user who looks
straight into the camera.
[0417] As the user looks straight into the camera, the face
verifier 710 verifies that the user's face in the second image are
indeed symmetric, as described in further detail hereinabove.
[0418] Consequently, data which includes the second image (or
featured extracted from the second image) is forwarded to the face
identifier 720, which identifies the user. The data may be
forwarded over a wide area network 715, say the internet, as
described in further detail hereinabove.
[0419] Upon successful identification of the user a relevant
message is presented to the user, say a welcome message, as
illustrated in FIG. 17C.
[0420] It is expected that during the life of this patent many
relevant devices and systems will be developed and the scope of the
terms herein, particularly of the terms "Camera", "Image", "Photo",
"Computer", "Network", "Internet", and "Intranet", is intended to
include all such new technologies a priori.
[0421] It is appreciated that certain features of the invention,
which are, for clarity, described in the context of separate
embodiments, may also be provided in combination in a single
embodiment. Conversely, various features of the invention, which
are, for brevity, described in the context of a single embodiment,
may also be provided separately or in any suitable
sub-combination.
[0422] Although the invention has been described in conjunction
with specific embodiments thereof, it is evident that many
alternatives, modifications and variations will be apparent to
those skilled in the art. Accordingly, it is intended to embrace
all such alternatives, modifications and variations that fall
within the spirit and broad scope of the appended claims.
[0423] All publications, patents and patent applications mentioned
in this specification are herein incorporated in their entirety by
reference into the specification, to the same extent as if each
individual publication, patent or patent application was
specifically and individually indicated to be incorporated herein
by reference. In addition, citation or identification of any
reference in this application shall not be construed as an
admission that such reference is available as prior art to the
present invention.
* * * * *