U.S. patent application number 13/235757 was filed with the patent office on 2012-02-16 for detecting red eye filter and apparatus using meta-data.
This patent application is currently assigned to DIGITALOPTICS CORPORATION EUROPE LIMITED. Invention is credited to Petronel Bigioi, Peter Corcoran, Michael J. DeLuca, Alexei Pososin, Yury Prilutsky, Eran Steinberg.
Application Number | 20120038788 13/235757 |
Document ID | / |
Family ID | 34135579 |
Filed Date | 2012-02-16 |
United States Patent
Application |
20120038788 |
Kind Code |
A1 |
DeLuca; Michael J. ; et
al. |
February 16, 2012 |
Detecting Red Eye Filter and Apparatus Using Meta-Data
Abstract
A method of filtering a red-eye phenomenon from an acquired
digital image including a multiplicity of pixels indicative of
color, the pixels forming various shapes of the image, includes
analyzing meta-data information, determining one or more regions
within the digital image suspected as including red eye artifact,
and determining, based at least in part on the meta-data analysis,
whether the regions are actual red eye artifact. The meta-data
information may include information describing conditions under
which the image was acquired, captured and/or digitized,
acquisition device-specific information, and/film information.
Inventors: |
DeLuca; Michael J.; (Boca
Raton, FL) ; Prilutsky; Yury; (San Mateo, CA)
; Steinberg; Eran; (San Francisco, CA) ; Corcoran;
Peter; (Claregalway, IE) ; Bigioi; Petronel;
(Galway, IE) ; Pososin; Alexei; (Galway,
IE) |
Assignee: |
DIGITALOPTICS CORPORATION EUROPE
LIMITED
Galway
IE
|
Family ID: |
34135579 |
Appl. No.: |
13/235757 |
Filed: |
September 19, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12613457 |
Nov 5, 2009 |
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13235757 |
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10635862 |
Aug 5, 2003 |
7630006 |
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12613457 |
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10170511 |
Jun 12, 2002 |
7042505 |
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10635862 |
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08947603 |
Oct 9, 1997 |
6407777 |
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10170511 |
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Current U.S.
Class: |
348/222.1 ;
348/E5.031; 382/167 |
Current CPC
Class: |
H04N 5/2354 20130101;
G06T 7/408 20130101; G06T 5/40 20130101; H04N 5/357 20130101; H04N
1/62 20130101; G06T 7/90 20170101; G06T 2207/30216 20130101; H04N
1/624 20130101; G06T 2207/30201 20130101; G06T 2207/10024 20130101;
G06T 2207/30041 20130101; G06T 5/005 20130101; H04N 1/6086
20130101; G06T 5/008 20130101; G06T 7/0002 20130101; G06K 9/0061
20130101; H04N 5/217 20130101 |
Class at
Publication: |
348/222.1 ;
382/167; 348/E05.031 |
International
Class: |
H04N 5/228 20060101
H04N005/228; G06K 9/00 20060101 G06K009/00 |
Claims
1. A method of detecting and correcting an eye defect within an
acquired digital image comprising a multiplicity of pixels
indicative of luminance and color, the pixels forming various
shapes within the image, the method comprising: acquiring a digital
image including said multiplicity of pixels indicative of luminance
and color; detecting a candidate eye defect region based on color,
luminance or shape, or combinations thereof, within the digital
image; determining a size of the candidate eye defect region;
analyzing anthropometric information including statistics relating
to at least one relationship between said size of said candidate
eye defect region and a location of a second detected eye, lips,
nostrils or a surrounding face, or combinations thereof;
determining a distance between an image acquisition device that
acquired the digital image and an eye of a subject that comprises
said eye defect region; and determining, based at least in part on
said distance, said size and said anthropometric information,
whether said candidate eye defect region is suspected as including
an eye defect region.
2. The method of claim 1, further comprising analyzing meta-data
information including image acquisition device-specific
information, including f-stop, aperture, exposure, gain, white
balance or color transformation, or combinations thereof, and
wherein the determining whether said candidate eye defect region is
suspected as including an eye defect region if further based on
said meta-data.
3. The method of claim 2, wherein the meta-data further includes
information describing conditions under which the image was
acquired.
4. A method of determining an age of a face within an acquired
digital image comprising a multiplicity of pixels indicative of
luminance and color, the pixels forming various shapes within the
image, the method comprising: acquiring a digital image including
said multiplicity of pixels indicative of luminance and color;
detecting a candidate eye defect region based on color, luminance
or shape, or combinations thereof, within the digital image;
determining a size of the candidate eye defect region; analyzing
anthropometric information including statistics relating to at
least one relationship between said size of said candidate eye
defect region and a location of a second detected eye, lips,
nostrils or a surrounding face, or combinations thereof;
determining a distance between an image acquisition device that
acquired the digital image and an eye of a subject that comprises
said candidate eye defect region; and determining, based at least
in part on said distance, said size and said anthropometric
information, an age of a subject whose eye comprises said candidate
eye defect region.
5. The method of claim 4, further comprising analyzing meta-data
information including image acquisition device-specific
information, including f-stop, aperture, exposure, gain, white
balance or color transformation, or combinations thereof, and
wherein the determining said age of said subject whose eye
comprises said candidate eye defect region is further based on said
meta-data.
6. The method of claim 5, wherein the meta-data further includes
information describing conditions under which the image was
acquired.
7. The method of claim 4, further comprising determining, based at
least in part on said distance, said size and said anthropometric
information, whether said candidate eye defect region is suspected
as including an eye defect region.
8. One or more non-transitory processor-readable media having code
embodied therein to program one or more processors to perform a
method of detecting and correcting an eye defect within an acquired
digital image comprising a multiplicity of pixels indicative of
luminance and color, the pixels forming various shapes within the
image, wherein the method comprises: acquiring a digital image
including said multiplicity of pixels indicative of luminance and
color; detecting a candidate eye defect region based on color,
luminance or shape, or combinations thereof, within the digital
image; determining a size of the candidate eye defect region;
analyzing anthropometric information including statistics relating
to at least one relationship between said size of said candidate
eye defect region and a location of a second detected eye, lips,
nostrils or a surrounding face, or combinations thereof;
determining a distance between an image acquisition device that
acquired the digital image and an eye of a subject that comprises
said eye defect region; and determining, based at least in part on
said distance, said size and said anthropometric information,
whether said candidate eye defect region is suspected as including
an eye defect region.
9. The one or more non-transitory processor-readable media of claim
8, wherein the method further comprises analyzing meta-data
information including image acquisition device-specific
information, including f-stop, aperture, exposure, gain, white
balance or color transformation, or combinations thereof, and
wherein the determining whether said candidate eye defect region is
suspected as including an eye defect region if further based on
said meta-data.
10. The one or more non-transitory processor-readable media of
claim 9, wherein the meta-data further includes information
describing conditions under which the image was acquired.
11. One or more non-transitory processor-readable media having code
embodied therein to program one or more processors to perform a
method of determining an age of a face within an acquired digital
image comprising a multiplicity of pixels indicative of luminance
and color, the pixels forming various shapes within the image,
wherein the method comprises: acquiring a digital image including
said multiplicity of pixels indicative of luminance and color;
detecting a candidate eye defect region based on color, luminance
or shape, or combinations thereof, within the digital image;
determining a size of the candidate eye defect region; analyzing
anthropometric information including statistics relating to at
least one relationship between said size of said candidate eye
defect region and a location of a second detected eye, lips,
nostrils or a surrounding face, or combinations thereof;
determining a distance between an image acquisition device that
acquired the digital image and an eye of a subject that comprises
said candidate eye defect region; and determining, based at least
in part on said distance, said size and said anthropometric
information, an age of a subject whose eye comprises said candidate
eye defect region.
12. The one or more non-transitory processor-readable media of
claim 11, wherein the method further comprises analyzing meta-data
information including image acquisition device-specific
information, including f-stop, aperture, exposure, gain, white
balance or color transformation, or combinations thereof, and
wherein the determining said age of said subject whose eye
comprises said candidate eye defect region is further based on said
meta-data.
13. The one or more non-transitory processor-readable media of
claim 12, wherein the meta-data further includes information
describing conditions under which the image was acquired.
14. The one or more non-transitory processor-readable media of
claim 11, wherein the method further comprises determining, based
at least in part on said distance, said size and said
anthropometric information, whether said candidate eye defect
region is suspected as including an eye defect region.
15. A digital image acquisition device, comprising: a lens, an
image sensor, a processor, and a memory having program code
embodied therein for programming the processor to perform a method
of detecting and correcting an eye defect within an acquired
digital image comprising a multiplicity of pixels indicative of
luminance and color, the pixels forming various shapes within the
image, wherein the method comprises: acquiring a digital image
including said multiplicity of pixels indicative of luminance and
color; detecting a candidate eye defect region based on color,
luminance or shape, or combinations thereof, within the digital
image; determining a size of the candidate eye defect region;
analyzing anthropometric information including statistics relating
to at least one relationship between said size of said candidate
eye defect region and a location of a second detected eye, lips,
nostrils or a surrounding face, or combinations thereof;
determining a distance between an image acquisition device that
acquired the digital image and an eye of a subject that comprises
said eye defect region; and determining, based at least in part on
said distance, said size and said anthropometric information,
whether said candidate eye defect region is suspected as including
an eye defect region.
16. The device of claim 15, wherein the method further comprises
analyzing meta-data information including image acquisition
device-specific information, including f-stop, aperture, exposure,
gain, white balance or color transformation, or combinations
thereof, and wherein the determining whether said candidate eye
defect region is suspected as including an eye defect region if
further based on said meta-data.
17. The device of claim 16, wherein the meta-data further includes
information describing conditions under which the image was
acquired.
18. A digital image acquisition device, comprising: a lens, an
image sensor, a processor, and a memory having program code
embodied therein for programming the processor to perform a method
of determining an age of a face within an acquired digital image
comprising a multiplicity of pixels indicative of luminance and
color, the pixels forming various shapes within the image, wherein
the method comprises: acquiring a digital image including said
multiplicity of pixels indicative of luminance and color; detecting
a candidate eye defect region based on color, luminance or shape,
or combinations thereof, within the digital image; determining a
size of the candidate eye defect region; analyzing anthropometric
information including statistics relating to at least one
relationship between said size of said candidate eye defect region
and a location of a second detected eye, lips, nostrils or a
surrounding face, or combinations thereof; determining a distance
between an image acquisition device that acquired the digital image
and an eye of a subject that comprises said candidate eye defect
region; and determining, based at least in part on said distance,
said size and said anthropometric information, an age of a subject
whose eye comprises said candidate eye defect region.
19. The one or more non-transitory processor-readable media of
claim 18, wherein the method further comprises analyzing meta-data
information including image acquisition device-specific
information, including f-stop, aperture, exposure, gain, white
balance or color transformation, or combinations thereof, and
wherein the determining said age of said subject whose eye
comprises said candidate eye defect region is further based on said
meta-data.
20. The one or more non-transitory processor-readable media of
claim 19, wherein the meta-data further includes information
describing conditions under which the image was acquired.
21. The one or more non-transitory processor-readable media of
claim 18, wherein the method further comprises determining, based
at least in part on said distance, said size and said
anthropometric information, whether said candidate eye defect
region is suspected as including an eye defect region.
Description
PRIORITY
[0001] This application is a Continuation of U.S. patent
application Ser. No. 12/613,457, filed Nov. 5, 2009; which is a
Continuation of U.S. patent application Ser. No. 10/635,862, filed
Aug. 5, 2003, now U.S. Pat. No. 7,630,006; which is a
Continuation-in-part of U.S. patent application Ser. No.
10/170,511, filed Jun. 12, 2002, now U.S. Pat. No. 7,042,505; which
is a Continuation of U.S. patent application Ser. No. 08/947,603,
filed Oct. 9, 1997, now U.S. Pat. No. 6,407,777, which is hereby
incorporated by reference.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention relates generally to digital
photography using flash, and specifically to filtering "Red Eye"
artifacts from digital images shot by digital cameras or scanned by
a digital scanner as part of an image acquisition process or an
image printing process.
[0004] 2. Description of the Related Art
[0005] i. Red Eye Phenomenon
[0006] "Red-eye" is a phenomenon in flash photography where a flash
is reflected within a subject's eye and appears in a photograph as
a red dot where the black pupil of the subject's eye would normally
appear. The unnatural glowing red of an eye is due to internal
reflections from the vascular membrane behind the retina, which is
rich in blood vessels. This objectionable phenomenon is well
understood to be caused in part by a small angle between the flash
of the camera and the lens of the camera. This angle has decreased
with the miniaturization of cameras with integral flash
capabilities. Additional contributors include the relative
closeness of the subject to the camera, iris color where light eyes
are more susceptible to this artifact and low ambient light levels
which means the pupils are dilated.
[0007] The red-eye phenomenon can be somewhat minimized by causing
the iris to reduce the opening of the pupil. This is typically done
with a "pre-flash", a flash or illumination of light shortly before
a flash photograph is taken or a strong additional light source.
This causes the iris to close. Unfortunately, these techniques
typically delay the photographic exposure process by 0.5 second or
more to allow for the pupil to contract. Such delay may cause the
user to move, the subject to turn away, etc. Therefore, these
techniques, although somewhat useful in removing the red-eye
artifact, can cause new unwanted results.
[0008] ii. Digital Cameras and Red Eye Artifacts
[0009] Digital cameras are becoming more popular and smaller in
size. Digital cameras have several advantages over film cameras,
e.g. eliminating the need for film as the image is digitally
captured and stored in a memory array for display on a display
screen on the camera itself. This allows photographs to be viewed
and enjoyed virtually instantaneously as opposed to waiting for
film processing. Furthermore, the digitally captured image may be
downloaded to another display device such as a personal computer or
color printer for further enhanced viewing. Digital cameras include
microprocessors for image processing and compression and camera
systems control. Nevertheless, without a pre-flash, both digital
and film cameras can capture the red-eye phenomenon as the flash
reflects within a subject's eye. Thus, what is desired is a method
of eliminating red-eye phenomenon within a miniature digital camera
having a flash without the distraction of a pre-flash.
[0010] An advantage of digital capture devices is that the image
contains more data than the traditional film based image has. Such
data is also referred to as meta-data and is usually saved in the
header of the digital file. The meta-data may include information
about the camera, the user, and the acquisition parameters.
[0011] iii. Digital Scanning and Red Eye Artifacts
[0012] In many cases images that originate from analog devices like
film are being scanned to create a digital image. The scanning can
be either for the purpose of digitization of film based images into
digital form, or as an intermediate step as part of the printing of
film based images on a digital system. Red Eye phenomenon is a well
known problem even for film cameras, and in particular point and
shoot cameras where the proximity of the flash and the lens is
accentuated. When an image is scanned from film, the scanner may
have the option to adjust its scanning parameters in order to
accommodate for exposure and color balance. In addition, for
negative film, the scanner software will reverse the colors as well
as remove the orange, film base mask of the negative.
[0013] The so-called meta data for film images is generally more
limited than for digital cameras. However, most films include
information about the manufacturer, the film type and even the
batch number of the emulsion. Such information can be useful in
evaluating the raw, uncorrected color of eyes suffering from red
eye artifacts.
[0014] iv. Red-Eye Detection and Correction Algorithms
[0015] Red-eye detection algorithms typically include detecting the
pupil and detecting the eye. Both of these operations may be
performed in order to determine if red-eye data is red-eye or if an
eye has red-eye artifact in it. The success of a red eye detection
algorithm is generally dependent on the success of a correct
positive detection and a minimal false detection of the two. The
detection is primarily done on image data information, also
referred to as pixel-data. However, there is quite a lot of
a-priori information when the image is captured and the nature of
the artifact that can be utilized. Such information relies on both
anthropometric information as well as photographic data.
[0016] v. Anthropometry
[0017] Anthropometry is defined as the study of human body
measurement for use in anthropological classification and
comparison. Such data, albeit extremely statistical in nature, can
provide good indication as to whether an object is an eye, based on
analysis of other detected human objects in the image.
[0018] vi. Bayesian Statistics
[0019] A key feature of Bayesian methods is the notion of using an
empirically derived probability distribution for a population
parameter such as anthropometry. In other words, Bayesian
probability takes account of the system's propensity to misidentify
the eyes, which is referred to as `false positives`. The Bayesian
approach permits the use of objective data or subjective opinion in
specifying an a priori distribution. With the Bayesian approach,
different individuals or applications might specify different prior
distributions, and also the system can improve or have a
self-learning mode to change the subjective distribution. In this
context, Bayes' theorem provides a mechanism for combining an a
priori probability distribution for the states of nature with new
sample information, the combined data giving a revised probability
distribution about the states of nature, which can then be used as
an a priori probability with a future new sample, and so on. The
intent is that the earlier probabilities are then used to make ever
better decisions. Thus, this is an iterative or learning process,
and is a common basis for establishing computer programs that learn
from experience.
Mathematically,
[0020] While conditional probability is defined as:
P ( A | B ) = P ( A B ) P ( B ) ##EQU00001##
In Bayesian statistics:
P ( A | B ) = P ( B | A ) P ( B ) P ( A ) ##EQU00002##
Alternatively a verbal way of representing it is:
Posterior = Likelihood .times. Prioir Normalizing_Factor
##EQU00003##
Or with a Likelihood function L( ), over a selection of events,
which is also referred to as the Law of Total Probability:
P ( B i | A ) = L ( A | B i ) P ( B ) all - j L ( A | B j ) P ( B j
) ##EQU00004##
A Venn diagram is depicted in FIG. 8-b.
SUMMARY OF THE INVENTION
[0021] A method of filtering a red-eye phenomenon from an acquired
digital image including a multiplicity of pixels indicative of
color, the pixels forming various shapes of the image, is provided.
The method includes analyzing meta-data information including
information describing conditions under which the image was
acquired and/or acquisition device-specific information;
determining one or more regions within said digital image suspected
as including red eye artifact; and determining, based at least in
part on said meta-data analysis, whether said regions are actual
red eye artifact.
[0022] The method may further include obtaining anthropometrical
information of human faces and the determining, based at least in
part on said meta-data analysis, whether the regions are actual red
eye artifact, being based further on the anthropometrical
information.
[0023] The filtering may be executed within a portable image
acquisition device, having no photographic film. The filtering may
be executed as a post-processing step on an external computation
device.
[0024] The meta-data information describing the conditions under
which the image was acquired may include an indication of whether a
flash was used when the image was acquired and/or an aperture at
the time of the acquisition. The acquisition device information may
include sensor size and/or a spectral response of a sensor of the
acquisition device. The acquisition device information may further
or alternatively include a color transformation from raw sensor
pixel values to saved image pixel values. A color of the pixels
indicative of red eye color may be calculated based on a spectral
response and a color transformation.
[0025] A lens may be used to capture the image. The meta-data
information may include a focal length of the lens and/or a
focusing distance of the lens at time of acquisition.
[0026] The actual red eye artifact may be determined based on
calculated expected size of the red eye artifact based on the
meta-data information including the acquisition device information.
The calculated expected size of the red eye artifact may be defined
as a range with a density probability function. The range may be
determined by depth of field which is a function of said aperture
setting. The method may further include obtaining anthropometrical
information of human faces and the determining, based at least in
part on the meta-data analysis, whether the regions are actual red
eye artifact, may be based further on the anthropometrical
information. The range may be determined by a statistical
distribution of the anthropometrical information.
[0027] The determining whether the regions are actual red eye
artifact may be performed as a probability determination process
based upon multiple criteria. The method may further include
adjusting a pixel color within any of the regions wherein red eye
artifact is determined and outputting an adjusted image to a
printer. The pixel color correcting may also be performed within
the printer. The method may further include adjusting a pixel color
within any of the regions wherein red eye artifact is determined
and outputting an adjusted image.
[0028] A digital apparatus having no photographic film is also
provided. The apparatus includes a source of light for providing
illumination during image capturing; a digital image capturing
apparatus; at least one of an image display and an image output;
and a red-eye filter for modifying pixels indicative of a red-eye
phenomenon within the at least one of the image display and the
image output.
[0029] The apparatus may further include memory for recording the
image after applying the filter module for modifying pixels as a
modified image. The modified pixels may be stored directly in the
image by replacing the pixels within the image indicative of
red-eye phenomenon to create the modified image. The modified
pixels may be stored as an overlay of the image thus preserving the
original image. The modified pixels may be processed by an external
device. The external device may be a personal computer and/or a
printer.
[0030] The apparatus may further include an image output for
downloading an integral image display for printing the image
modified by the red-eye filter. The red-eye correction module may
generate an overlay for the pixels indicative of the red-eye
phenomenon of the captured image for the at least one of image
display and image output.
[0031] The pixels indicative of the red-eye phenomenon may have a
color and shape indicative of the red-eye phenomenon and the image
may be modified to change the color to a black color. Also, the
source of light may selectively provide illumination during image
capturing, and the red-eye filter may be enabled to modify the
image in response to the source of light providing illumination
during image capturing. The apparatus may include an exposure
control means for determining if the image was captured in a
condition conducive to the red-eye phenomenon and for generating a
red-eye signal in response thereto. The red-eye filter may be
further enabled in response to the red-eye signal.
[0032] The red-eye filter may further include a false-detection
avoidance apparatus which enables modification of the pixels
indicative of the red-eye phenomenon in response to an absence of
color indicative of the red-eye phenomenon with in a vicinity of
and exclusive to the pixels. The red-eye filter may further include
a false-detection avoidance apparatus which enables modification of
the pixels in response to one or more of a substantially white
colored region, an iris ring and an eyebrow line within a vicinity
of the pixels. The red-eye filter may detect the pixels within the
image indicative of a red-eye phenomenon based on one or more of a
substantially white colored region, an iris ring and an eyebrow
line within a vicinity of the area.
[0033] The red-eye filter may include a pixel locator for locating
the pixels having a color indicative of the red-eye phenomenon; a
shape analyzer for determining if a grouping of at least a portion
of the pixels located by the pixel locator include a shape
indicative of the red-eye phenomenon; and a pixel modifier for
modifying the color of the pixels within the grouping. The
false-detection analyzer may further process the image in a
vicinity of the grouping for details indicative of an eye, and for
enabling the pixel modifier in response thereto. The apparatus may
further include an exposure analyzer for determining if the image
was recorded in a condition indicative of the red-eye phenomenon.
The red-eye filter may further include an exposure analyzer for
determining if the image was recorded in a condition indicative of
the red-eye phenomenon.
[0034] The exposure analyzer may determine if the image was
recorded in a condition indicative of the red-eye phenomenon
including determining whether the light source was used during
image recording. The exposure analyzer may determine if the image
was recorded in a condition indicative of the red-eye phenomenon
including determining whether low ambient lighting conditions
existed during image recording. The exposure analyzer may determine
if the image was recorded in a condition indicative of the red-eye
phenomenon. The exposure analyzer may further include a distance
analyzer for determining if the subject was at a relatively close
distance to the apparatus during image recording.
[0035] A portable digital image acquisition apparatus having no
photographic film is also provided. The apparatus includes an
integral flash for providing illumination during image recording; a
digital image capturing apparatus for recording an image; and a
red-eye filter for modifying an area within the image indicative of
a red-eye phenomenon.
[0036] The apparatus may further include an integral image display
for displaying the modified image. The area may have a color and
shape indicative of the red-eye phenomenon and the image may be
modified to change the color to a black color. The integral flash
may selectively provide illumination during image recording, and
the red-eye filter may be enabled to modify the image in response
to the integral flash providing illumination during image
recording.
[0037] The apparatus may include an exposure control means for
determining if the image was recorded in a condition conducive to
the red-eye phenomenon and for generating a red-eye signal in
response thereto. The red-eye filter may be further enabled in
response to the red-eye signal.
[0038] The red-eye filter may further include a falsing avoidance
apparatus which enables modification of the area in response to an
absence of color indicative of the red-eye phenomenon within a
vicinity of and exclusive to the area. The red-eye filter may
further include a falsing avoidance apparatus which enables
modification of the area in response to a substantially white
colored region within a vicinity of the area.
[0039] The red-eye filter may include a pixel locator for locating
pixels having a color indicative of the red-eye phenomenon; a shape
analyzer for determining if a grouping of at least a portion of the
pixels located by the pixel locator comprise a shape indicative of
the red-eye phenomenon; and a pixel modifier for modifying the
color of the pixels within the grouping. The red-eye filter may
further include a falsing analyzer for further processing the image
in a vicinity of the grouping for details indicative of an eye, and
for enabling the pixel modifier in response thereto. The red-eye
filter may further include an exposure analyzer for determining if
the image was recorded in a condition indicative of the red-eye
phenomenon.
[0040] A method of filtering a red-eye phenomenon from an acquired
digital image comprising a multiplicity of pixels indicative of
color, the pixels forming various shapes of the image, is further
provided. The method includes analyzing meta-data information
including information describing conditions under which the image
was acquired, digitized and/or captured; determining one or more
regions within the digital image suspected as including red eye
artifact; and determining, based at least in part on the meta-data
analysis, whether the regions are actual red eye artifact.
[0041] The method may further include obtaining anthropometrical
information of human faces and the determining, based at least in
part on said meta-data analysis, whether the regions are actual red
eye artifact, may be based further on the anthropometrical
information. The filtering method may be executed within a portable
image acquisition device, having no photographic film. The
filtering method may be executed as a post-processing step on an
external computation device. The meta-data information describing
the conditions under which the image was acquired may include an
indication of whether a flash was used when the image was acquired.
The determining whether the regions are actual red eye artifact may
be performed as a probability determination process based upon
multiple criteria. The method may include adjusting a pixel color
within any of the regions wherein red eye artifact is determined
and outputting an adjusted image to a printer. The pixel color
correction may also be performed within the printer.
BRIEF DESCRIPTION OF THE DRAWINGS
[0042] FIG. 1 shows a block diagram of an acquisition device
operating in accordance with a preferred embodiment.
[0043] FIG. 2 illustrates a high level workflow of detecting red
eye artifacts in digital images in accordance with a preferred
embodiment.
[0044] FIGS. 3a-3d schematically depicts a light sensor, and the
formation of a digital pixelated image on it, in accordance with a
preferred embodiment.
[0045] FIG. 4 describes a process of collecting, forwarding and
analyzing meta-data as part of red-eye detection in accordance with
a preferred embodiment.
[0046] FIG. 5 illustrates by means of geometric optics, a
relationship between an object and an image based on a distance to
the object and the focal length, where the focal length is the
distance from the image principal plane of the optical system to
the image focal plane, which is the plane where the image of the
object situated at infinity is formed.
[0047] FIG. 6 illustrates a relationship between focal length of a
lens and depth of field, and an object size as it appears on an
image.
[0048] FIGS. 7a-7c illustrate some anthropometric measurements of a
human face for an adult male and female.
[0049] FIGS. 8a-8b show a workflow diagram describing a statistical
analysis of an image using anthropometric data in accordance with a
preferred embodiment.
[0050] FIG. 9 depicts a spectral response of an acquisition system
based on spectral sensitivity curves of a hypothetical three color
sensor, the spectral distribution of a generic light source and the
spectral characteristics of a object being photographed, in
accordance with a preferred embodiment.
INCORPORATION BY REFERENCE
[0051] What follows is a cite list of references which are, in
addition to those references cited above and below herein, and
including that which is described as background, the invention
summary, brief description of the drawings, the drawings and the
abstract, hereby incorporated by reference into the detailed
description of the preferred embodiments below, as disclosing
alternative embodiments of elements or features of the preferred
embodiments not otherwise set forth in detail below. A single one
or a combination of two or more of these references may be
consulted to obtain a variation of the preferred embodiments
described in the detailed description below. Further patent, patent
application and non-patent references are cited in the written
description and are also incorporated by reference into the
preferred embodiment with the same effect as just described with
respect to the following references:
[0052] U.S. Pat. Nos. 4,285,588, 5,016,107, 5,070,355, 5,202,720,
5,537,516, 5,452,048, 5,748,764, 5,761,550, 5,781,650, 5,862,217,
5,862,218, 5,991,549, 6,006,039, 6,433,818, 6,510,520, 6,516,154,
6,505,003, 6,501,911, 6,496,655, 6,429,924, 6,252,976,
6,278,491;
[0053] United States published applications nos. 2003/0058349,
2003/0044177, 2003/0044178, 2003/0044070, 2003/0044063,
2003/0025811, 2002/0150306, 2002/0041329, 2002/0141661, and
2002/0159630;
[0054] PCT published applications no. WO 03/026278, WO 99/17254;
and WO 01/71421; and
[0055] Japanese patents no. JP 04-192681, JP 2000/134486, and JP
2002/271808; and
[0056] European patents no. EP 0 884 694 A1, EP 0 911 759 A2,3, EP
1 293 933 A1, EP 1 199 672 A2, EP 1 288 858 A1, EP 1 288 859 A1,
and EP 1 288 860 A1; and
[0057] Matthew Gaubatz, et al., "Automatic Red-eye Detection and
correction", IEEE ICIP, 2002, pp. I-804-1-807.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0058] Preferred embodiments described below include methods for
detecting red eye artifacts in digital images. Methods are also
described for utilizing meta-data gathered as part of the image
acquisition to remove such red-eye artifacts. In addition, methods
for enhancing the accuracy of detection of red eye artifacts based
on a-priori knowledge of the camera sensor, the acquisition
mechanism and the color transformation are described. Methods are
described for enhancing the speed of detection of red eye artifacts
in digital images, and for reducing the amount of false detection
of regions suspected to be red-eye artifacts. A method for
user-selected tradeoff between the reduction of false detection and
the improvement of positive detection is also described. In
addition, a way to estimate the size of faces is provided, and in
particular the eyes in an image and in particular the size of eyes
in faces based on the acquisition data. A way to improve the
detection of the eyes based on anthropometric analysis of the image
is also provided. An improvement is described for the detection of
the eyes based on a Bayesian statistical approach. An improvement
is also described for the detection of the red eye artifacts based
a priori knowledge of the film manufacturer, the film type and/or
the emulsion batch of the film. An improvement is also described
for the detection of the eye artifact based on a priori knowledge
of the scanner its light source and the color sensors of the
scanner.
[0059] In one embodiment, a digital camera has a built in flash, an
image acquisition mechanism and a way to save the acquired data.
The methods of the preferred embodiments are generally applicable
to digital image acquisition devices, such as digital cameras and
scanners, and to and output devices such as printers and electronic
storage devices. When the terms digital camera and output device or
printer are used, it is generally meant to more broadly,
respectively include digital image acquisition devices and digital
data output devices.
[0060] A printer that receives image data from an original image
acquisition device such as a digital camera or scanner may include
a display that shows the image, or may be configurable to be cable,
rf, or otherwise connected to a display. In this way, the image may
be previewed before printing, and if desired, corrected and
previewed again until the image is as desired for printing. Another
alternative is to permit the image to be printed as a thumbnail
with a preview of the red eye corrected regions to save on printing
time and money. These regions of interest as to red eye correction
may be circled or otherwise indicated in the printer viewer and or
in the printed thumbnail. In a case where the camera itself
includes the red eye correction software, firmware, and/or memory
or other electronic component circuitry, then such preview and/or
thumbnail capability may be included within the camera that may
itself be cable, rf, network and/or otherwise connected to the
printer.
[0061] The digital camera or other acquisition device preferably
has the capability of analyzing and processing images.
Alternatively, the processing of the images can be done outside of
the camera on a general purpose or specialized computer after
downloading the images or on a device that is acting as a hosting
platform for the digital camera. Such a device may be, but is not
limited to, a hand held PC, a print server, a printer with built in
processing capability, or cell phone equipped with a digital
camera. Alternatively the acquisition process can be of an analog
image, such as scanning of a film based negative or reversal film,
or scanning of a photographic print.
[0062] The accuracy of a detection process may be measured by two
parameters. The former is the correct detection, which relates to
the percentage of objects correctly detected. The second parameter
for evaluating successful detection is the amount of
mis-classifications, which is also defined as false detections or
beta-error. False detections relate to the objects falsely
determined to have the specific characteristics, which they do not
possess.
[0063] Overall, the goal of a successful detection process is to
improve the accuracy of correct detections while minimizing the
percentage of false detections. In many cases there is a tradeoff
between the two. When the search criterion is relaxed, more images
are detected but at the same time, more false detections are
typically introduced, and vice versa.
[0064] In order to improve the accuracy of the red eye detection
and correction, a preferred embodiment utilizes a priori
information about the camera or camera-specific information,
anthropometric information about the subject, and information
gathered as part of the acquisition process. That is, although
information gathered as part of the acquisition process may relate
to the camera or other digital acquisition device used, information
relating to those parameters that are adjustable or that may change
from exposure to exposure, based on user input or otherwise, are
generally included herein as information relating to the
acquisition process. A priori or camera-specific information is
camera-dependent rather than exposure-dependent. For example,
a-priori information about the camera may include any of the color
sensitivity, spectral response or size of the camera sensor,
whether the sensor is CCD or CMOS, and color transformations from
the RAW data gathered by the sensor, e.g., CCD, to a known color
space such as RGB, the f-stop, or other camera-specific parameters
understood by those skilled in the art, or combinations thereof. In
the case of scanning such a-priori information may include the
color sensitivity curve of the film, the color sensitivity of the
scanner sensor, whether CCD or CMOS, whether linear or area
sensors, the color transformations from the RAW data gathered by
the scanner to a known color space such as RGB. Acquisition data
may include any of the focal distance as determined by the auto
focus mechanism of the digital camera, the power of the flash
including whether a flash was used at all, the focal length of the
lens at acquisition time, the size of the CCD, the depth of field
or the lens aperture, exposure duration, or other acquisition
parameters understood by those skilled in the art, or combinations
thereof. Anthropometric data may include first and higher order
statistics, which is an average and a variability of an expected
size and ratio between different parts of the human body, and
particularly the facial region.
[0065] Based on utilizing the aforementioned information, preferred
embodiments described herein achieve a more accurate detection of
the regions containing red eye artifacts. Based on this detection,
the processor, whether in the camera or on a different device, can
perform a correction step.
[0066] FIG. 1 is a components diagram in accordance with a
preferred embodiment. Block 100 describes the image acquisition
device which can be a digital camera in different packaging such as
a digital still camera, a lens connected to a hand held computer, a
cell phone with image capturing capability, a video camera with
still image capturing capability, etc.
[0067] In the image capture apparatus 100, there are a few
components shown in block form in FIG. 1. The first is the light
sensor 102 that can be a CCD, CMOS or any other object that
transforms light information into electronic encoding. Most cameras
are equipped with a built in flash 104, also referred to as a
strobe. In many cases, the camera strobe is physically close to the
lens, which tends to accentuate the occurrence and strength of the
red eye artifact. In addition, the camera is equipped with a lens
106. The relevant parameters of the lens during acquisition include
the aperture 114, or a f-stop, which primarily determines the depth
of field, the focal length 112 which determines the enlargement of
the image, and the focusing distance 116 which determines the
distance to the objects that the lens 106 was focused at.
[0068] Block 130 of FIG. 1 describes the red eye filter that
performs a process of detection and correction of the red eye
artifacts in accordance with a preferred embodiment. The process
can be done in the camera as part of the acquisition stage, in the
camera at a post processing stage, during the transferring of the
images from the camera to an external device such as a personal
computer, or on the external device as a post processing stage,
such as in the image transfer software or image editing
software.
[0069] The red eye filter includes two main stages. Block 132
describes a meta-data analysis module 132, where the image and the
probability for red eye artifacts are evaluated based on the
acquisition data and/or other meta-data. Block 138 describes the
pixel-based analysis where the image data is used. The pixel-based
analysis 138 preferably receives information from the meta-data
stage 132. Therefore, the decision on the pixel level may vary
based on the conditions under which the image was captured and/or
other meta-data. Block 160 describes the image storage component
160 that saves the image after the red eye correction
operation.
[0070] FIG. 2 is a workflow representation corresponding to the
preferred camera embodiment illustrated at FIG. 1. The image
capture stage is described in block 200. This operation includes
the pre-acquisition setup 210, where the user and/or the camera
determine preferred settings such as f-stop 212, flash on/off 214
and/or focal length 216. The image capture stage 200 also includes
acquisition or picture taking 226, and temporary storage in block
228 in its final form or in a raw form that corresponds to the
image as captured by the light sensor 102 of FIG. 1. As part of the
capture process, the camera determines the best acquisition
parameters in the pre-acquisition stage 210. Such parameters may
include the right exposure, including gain, white balance and color
transformation, and in particular aperture settings 212 and whether
to use flash 214. In addition, the user may decide on the focal
length 216 of the lens 106, which is also be referred to as the
zoom position.
[0071] The image after being stored in block 228, is then processed
for red eye 230 in accordance with a preferred embodiment, among
other stages of processing that may include color corrections,
compression, sharpening, etc. The red eye filter preferably
includes two main operations. The red eye detection 240 and red eye
correction 250.
[0072] The red eye detection 240 includes a first stage of
analyzing the peripheral or external data, or meta-data 242, a
stage of transferring the revised data 244, and the specific red
eye detection 246, based on pixel analysis.
[0073] The red eye correction is illustrated at FIG. 2 as the
operation 250 where any image modifications based on the results of
the detection stage 240, are applied to the image. At this stage
250, correction may be burned into the data 252, thus replacing the
damaged pixels, saved as a list of the pixels that need to be
changed with their new value in the header of the image or
externally 254, and/or presented to the user 256, requesting the
user to take an action in order to apply the corrections, or a
combination of these operations. The image, with the corrections
applied as described in 240, is then preferably saved in block
260.
[0074] FIGS. 3a-3d illustrates in detail the image as created on
the receptor 102 of FIG. 1, which is located at the image plane of
the optical system. Such receptor can be any electro-photosensitive
object such as CCD or CMOS.
[0075] FIG. 3a illustrates a grid type CCD. Each one of the smaller
squares (as illustrated by block 302) is a cell, which is sensitive
to light. The CCD size 304 is calculated as the diagonal of the
rectangle made of Width 306 and Height 308.
[0076] FIG. 3b illustrates how a face may be projected onto the
CCD. FIG. 3c illustrates how the image is pixelized, where the
continuous image is transformed into a grid based image.
[0077] FIG. 3d is more specific to the image as created by a human
eye. The image of the eye will include the iris 342 as well as the
pupil 344, which is usually the locations where red-eye artifacts
occur. The white part 346 of the eye is also a component of the
human eye illustrated at FIG. 3d and which can be used in red-eye
detection, particularly false-detection avoidance.
[0078] FIG. 4 illustrates various meta-data information that can be
utilized as part of a preferred embodiment as a priori input, and
the potential outcome of such data analysis. For example, blocks
412, 422, and 432 illustrate an operation of red-eye detection
relating to the use or non-use of flash. The information whether
the flash is used or not, Block 412, is forwarded at operation 422
to red-eye pre-processing 432 to determine whether there is reason
to launch the red-eye filter. If a Flash, as determined in 412 is
not used, there is preferably no reason to apply the redeye filter.
This is a reasonable estimation for consumer lever cameras where
most of the red eye is created, as described in the introduction,
by the small disparity between the strobe unit and the lens.
[0079] Blocks 414, 424, 434 describe a collection of acquisition
meta-data, wherein non-exhaustive examples are provided including
the distance to the object, the aperture, CCD size, focal length of
the lens and the depth of field. This data is usually recorded on
or with the image at acquisition. Based on this information, as
transferred to the filter at operation 424, the filter can
determine at operation 434, e.g., a range of potential sizes of red
eye regions.
[0080] Blocks 416, 426, 436 relate to specific information that is
unique to the camera. The color composition, e.g., of the image is
determined by a few parameters which include the CCD response
curves as illustrated in FIG. 9 (see below), and the potential
color transformations from the recorded, raw image data such as
color correction, gain adjustment and white balance to a known
color space such as RGB or YCC. Such transformations can be
presented in the form of lookup tables, transformation matrices,
color profiles, etc.
[0081] Based on the knowledge of the transfer from operation 426,
the software can better determine a more precise range of colors at
operation 436 that are good candidates for the red eye artifacts.
This information can advantageously narrow down the potential red
eye regions based on the variability of sensors and color
correction algorithms. It may also help to eliminate colors that,
without this knowledge, could be falsely identified as potential
red eye region candidates, but are not such in case of a specific
combination of sensor and color transformation.
[0082] FIG. 5 depicts illustrative information that can be gathered
to determine the relative size of the object. The ratio of the
image size divided by image distance, and the object size divided
by the object distance, are approximately equal, wherein the image
size divided by the object size is defined as the magnification of
the lens 106. If one knows three out of the four values, namely
focal length 112, distance to object 116, and object size 516, one
can estimate the size of the object:
Object size ( 516 ) distance to object ( 116 ) = image size ( 512 )
focal length ( 112 ) ##EQU00005##
[0083] If one knows three out of the four values, namely focal
length 112, distance to object 116, and object size 516 one can
estimate the image size 512:
Object size ( 516 ) = distance to object ( 116 ) image size ( 512 )
focal length ( 112 ) ##EQU00006##
[0084] However, the parameter values described above are usually
not known precisely. Instead, distributions of values can be
estimated based on different reasons as depicted in FIGS. 6, 7 and
8.
[0085] FIG. 6, illustrates the variability generated by the depth
of field. Depth of field is defined as the range of distances from
the camera to the objects where the images of the objects are
captured sufficiently sharp. For a fixed length lens, the depth of
field is a function of the aperture. The more open the aperture is,
the shallower the depth of field is.
[0086] As can be seen in FIG. 6, due to the fact that the depth of
field can be rather large, the distance to the objects still in
focus can vary. Therefore the parameter
Dis tan ce_to_Subject is rather a range: Dis tan
ce_to_Subject.sub.Close.sub.--.sub.range.ltoreq.Subject.ltoreq.Dis
tan ce_to_Subject.sub.Far.sub.--.sub.range
[0087] The reason why this information is important and has to be
taken into consideration is depicted in FIG. 6. In this case, two
objects, a tree 614 and a house 624 are located in close distance
616, and further away 626 respectively. Even though the tree, 614
and the house 634 are the same size, the sizes of the objects or
the projections of the objects on the image plane are different and
the tree image, 636 being closer to the camera appears much larger
than the house 646.
[0088] FIG. 7 includes some relevant anthropometrical values for
male and female averages. FIG. 7-a is an average male and FIG. 7-b
is an average adult female. For example, for adult male, 700, the
distance between the eyes, 714, is on average 2.36'', the distance
between the eyes and the nostrils, 724, is 1.5'' the width of the
head, 712 is 6.1'' etc.
[0089] However, this is only the first order approximation. There
is a second order approximation, which is the overall variability
of the values. Such variability once again needs to be calculated
into the formula.
Or:
[0090]
Subject_Size.sub.small.ltoreq.Subject_Size.ltoreq.Subject_Size.sub.-
Large
[0091] The object size, in order to be considered as a candidate
for being a face, and eye or any known object will be:
Subject_SizeSmall * Focal_Length Distance_To _Object Far_Range
.ltoreq. Object_Size .ltoreq. Subject_Size large * Focal_Length
Distance_To _Object Close_Range ##EQU00007##
[0092] Specifically, as seen in FIG. 7-c, the average size of an
eyeball, 770, is roughly 1'', or 24 mm, and the average size of the
iris, 772, is half in diameter to the full eye, or 0.5'' or 12 mm
in diameter. The pupil, 774 can be as small as a few millimeters,
and dilated to as large as the size of the iris. Fortunately, in
the case of red-eye artifacts, which happen primarily in low
lighting conditions that required a flash, the pupil will be on the
dilated side.
[0093] The variability in this case is not only for different
individuals, but also variability based on age. Luckily, in the
case of eyes, the size of the eye is relatively constant as the
person grows from a baby into an adult, this is the reason of the
striking effect of "big eyes" that is seen in babies and young
children. The average infant's eyeball measures approximately 191/2
millimeters from front to back, and as described above, grows to 24
millimeters on average during the person's lifetime. Based on this
data, in case of eye detection, the size of the object which is the
pupil which is part of the iris, is limited, when allowing some
variability to be:
9 mm.ltoreq.Size_Of_Iris.ltoreq.13 mm
[0094] The object size as calculated above is going to be in actual
physical size such as millimeters or inches. For this invention to
become useful, this information needs to be presented measured in
pixel sizes.
[0095] Returning to FIG. 3a, the size of the sensor is depicted by
304, which is the diagonal of the sensor. Based on that, and the
ratio between the width, 306 and the height, 308, the width and
height can be calculated as a Pythagorean triangle.
Sensor_Diagonal_Size= {square root over
(width.sup.2+Height.sup.2)}
Knowing the sensor resolution, the size of object can now be
translated into pixel size. For example: Given a 1/2 inch (12 mm)
CCD, with an aspect ratio of 2:3, and a 2,000.times.3,000 CCD
resolution: The width of the CCD is:
12 mm= {square root over
((2.alpha.).sup.2+(3.alpha.).sup.2)}{square root over
((2.alpha.).sup.2+(3.alpha.).sup.2)}= {square root over
(13)}.alpha.
3.alpha.=3.times.12/ {square root over
(13)}.apprxeq.3.times.3.3.apprxeq.10 mm
and therefore, for a 3000 pixel width, a 1 mm object size is equal
to roughly 300 pixels.
Or
[0096]
Image_Size.sub.in.sub.--.sub.pixels=Image_Size.sub.in.sub.--.sub.mi-
llimeters
[0097] Based on this formula, when an image is now detected, its
size in pixels is compared to the range allowed, and decided
whether the object is a candidate or not.
[0098] An example is depicted in FIG. 3d where a hypothetical eye
is displayed in pixels, and in this case, the iris 342, is roughly
11 pixels, and the pupil, 344, 6 pixels in diameter.
[0099] With the added knowledge of the distance to the object and
the focal length of the lens, this invention presents a decision
process capable of rejecting the objects, 346 that are not eyes and
selecting most likely candidates to be an eye based on the sizes of
the captured images of the objects.
[0100] FIG. 8 describes a preferred workflow to perform, the
analysis based on the sizes of objects, and in the case of human
beings, the anthropometrical analysis. The input is the acquisition
data 434, as described in FIG. 4, and human anthropometric data,
800 as depicted in FIGS. 7a and 7b.
[0101] Step 810 describes the calculation of potential size and
distribution of the objects, as corresponds to the camera
resolution. This process was fully defined above. Note that this
calculation can be done on the fly or alternatively pre-calculated
values can be stored in a database to speed up the processing.
[0102] When looking for eyes in an image, but not limited
specifically to eyes, given regions suspected as eyes, 820, a
preferred embodiment proposes to check, 830 whether the regions
fall within the size and distribution as calculated above in 820.
If the size is too large or too small, the system can determine,
890 that the probability for this object to be an eye is low.
However, this is a probabilistic result and not necessarily a
conclusive one. In other words, the specific region 820 has now low
probability assigned to it as a potential eye. If the region is
falling inside the allowed size, the probability, 880 are
raised.
[0103] This preferred embodiment describes additional steps to
refine the decision, or increase the probability, by analyzing
additional clues such as the existence of a second eye, 832, the
surrounding facial features, 834 such as the overall shape of the
face, the hair, neck etc., the existence of lips in proximity to
the eyes, 836, the nostrils 838 etc.
[0104] In each step, the question asked is whether the new feature
is part of the region, 840. If the reply is positive, then the
probability for identifying the area as an eye is raised, 850, and
if negative, the probability is reduced, 860. Of course, this
probabilistic approach can be useful to create a better set of
criteria in deciding whether the detected object is what the system
is looking for. In more detail, the detection process involves two
types of allowed errors also known as Type-I and Type-II errors, or
also referred to as .alpha.-error, which is the acceptable
probability of making a wrong decision, or a false positive and
.beta.-error, which is the acceptable probability of not detecting
at all. Based on this approach, the probability as decreased or
increased in steps 850 and 860 are always compared against the two
criteria .alpha. and .beta..
[0105] Alternatively to the classical statistical approach, this
analysis can be done using Bayesian approach. As defined above,
Bayesian probability can be calculated based on:
P ( B i | A ) = L ( A | B i ) P ( B ) all - j L ( A | B j ) P ( B j
) ##EQU00008##
[0106] This is further depicted in FIG. 8b. Specifically to this
embodiment, the events are:
A=Region detected is red eye, as depicted in Block 870 B.sub.j=the
various detected features as defined in blocks 872,874,876 and 878,
834,836 and 838. A.andgate.B.sub.j=Probability that the area is red
eye AND that another attribute is found. For example
[0107] If B.sub.i is the probability of detecting lips,
A.andgate.B.sub.j is the probability that the region is an eye and
that the lips are detected. P(B.sub.i|A) is the probability that
lips exist when eye is detected.
And
[0108] P(A|B.sub.j) is the probability of eye detection given the
probability of lips detection.
[0109] FIG. 9 illustrates a different kind of information that can
be very useful in determining the existence of red eye artifacts,
using the color sensitivity of the capturing system such as a
digital camera. Alternatively the capturing system may be analog
capture such as film followed by a digitization process such as
scanning.
[0110] The graph in FIG. 9 describes the relative response, 950 as
a function of the visual wavelength 910, of the three sensors for
blue, 932, Green 934, and Red 936, of a typical CCD type sensor.
Similar graph, although with different response curve describes the
response of the different layers for photographic film.
[0111] The x-axis, which is the wavelength range of the human
visual system, is expanded to include infrared and ultraviolet,
which may not be visible to the human eye but may record on a
sensor. The y-axis is depicted in relative value as opposed to an
absolute one. The three Red, Green, and Blue spectral response
functions as functions of the wavelength are defined respectively
as:
R(.lamda.), G(.lamda.), B(.lamda.)
[0112] Given a light source 940 defined as a spectral response
curve L(.lamda.), the light source 940 when reaching the three
different color sensors, or color pigments on film will generate a
response for each of the colors as defined mathematically as the
integral of the scalar multiplication of the curves. The range of
integration is from the low wavelength region UV to the highest
IR.
R = .intg. .lamda. - UV .lamda. - IR R .lamda. .times. L .lamda.
.lamda. , G = .intg. .lamda. - UV .lamda. - IR G .lamda. .times. L
.lamda. .lamda. ##EQU00009## B = .intg. .lamda. - UV .lamda. - IR B
.lamda. .times. L .lamda. .lamda. ##EQU00009.2##
to create a tristimulus value of {R, G, B}
[0113] Those skilled in the art are familiar with the fact that
different spectral responses may create the same tristimulus values
due to the scalar reduction from a 2 dimensional representation to
a single value. This effect is also known as Metamerizm which can
be a property of the sensor's/film's metamerizm, the human visual
system metamerizm, or the light source's metamerizm.
[0114] Due to the many variable parameters, it is relatively hard
to find a specific color that can be a fixed-reference-point in an
image. The reason is that the reflected colors are usually
dependent on many factors and especially on the ambient light.
However, Red Eye artifacts, as previously explained, are results of
the reflection of the strobe light, which has very well defined
characteristics, from the vascular membrane behind the retina,
which is rich in blood vessels. In most cases, the effect of the
external ambient light is relatively low, and the red-eye effect
can be considered as a self-illuminating object, with more precise
spectral characteristics than other objects. An example of such
spectral response, which is a combination, of the flash spectral
response, which is relatively broad and the blood vessels inside
the eye, is depicted in block 940.
[0115] Given the spectral sensitivity of the sensor:
R(.lamda.), G(.lamda.), B(.lamda.)
[0116] and the reflection of the flash light in the eye, as defined
by 950, E(.lamda.), the red eye tristimulus values for this
specific sensor are:
{ R , G , B } red - eye = .intg. .lamda. - UV .lamda. - IR { R , G
, B } .lamda. .times. L .lamda. .lamda. ##EQU00010##
[0117] This value of {R, G, B}.sub.red-eye is relatively constant
for a given camera. However, due to the difference in the response
between different sensors, these values are not constant across
different cameras. However, with the knowledge of the response
curves above, one can determine a much closer approximation of the
range or red colors based on this information. Note that it is not
only the value of the Red that may help in such determination, but
also the residual response of the red eye on the Green and even
less the blue sensor. One skilled in the art knows that most
cameras perform additional transformations for exposure and tone
reproduction for images before saving them into persistent storage.
An example of such transformation will be a concatenation of color
correction and tone reproduction as a function of the pixel
value:
[0118] Given a Raw pixel value of:
{R, G, B}.sub.RAW-CCD
[0119] as transformed via three lookup tables. For example for red
lookup table:
R-LUT(Raw-Pix): {input_values}.fwdarw.{output_values}
[0120] For example the Red lookup table R-Lut can be a gamma
function from 10 bit raw data to 8 bits as follows:
R.sub.LUT(Raw-Pix): {0 . . . 1024}.fwdarw.{0 . . . 256}
[0121] R.sub.LUT(x)=(R.sub.RAW-CCD/1024).sup.2.2*256
[0122] and the inverse function
R.sup.-1.sub.LuT(x)=(R.sub.LUT.sub.--.sub.RAw/256).sup.1/2.2*1024
[0123] the {R,G,B} values after transformed through the lookup
table will be:
{ R , G , B } LUT_RAW = { R LUT ( R RAW - CCD ) , G LUT ( G RAW -
CCD ) , B LUT ( B RAW - CCD ) } ##EQU00011## { R , G , B } new = {
R , G , B ) LUT_RAW .times. [ RR RG RB GR GG GB BR BG BB ]
##EQU00011.2##
[0124] With the internal knowledge of these transformations, one
can reverse the process, to reach the RAW values as defined
above.
{ R , G , B } LUT_RAW = [ RR RG RB GR GG GB BR BG BB ] - 1 .times.
{ R , G , B } NEW T ##EQU00012##
and {R, G, B}.sub.RAW={R.sup.-1.sub.LUT(R.sub.LUT.sub.--.sub.RAW),
G.sup.-1.sub.LUT(G.sub.lut.sub.--.sub.raw),
B.sup.-1.sub.LUT(B.sub.LUT.sub.--.sub.RAW)}
[0125] and the value of the raw tristimulus values can be then
determined and used for the exact matching. Similar transformations
are performed by digital scanners in order to correct for sub
optimal images such as underexposure, or wrong ambient light.
Reversing the process may be difficult in its pure mathematical
sense e.g. the conversion function may through the transformation
not be fully reversible. Such issues occur for example when the
pixel values are clipped or condensed. In such cases, there is a
need to define a numerical approximation to the inverse
function.
[0126] The preferred embodiments described above may be modified by
adding or changing operations, steps and/or components in many ways
to produce advantageous alternative embodiments. For example, there
are generally two approaches to removing red-eye from images. The
traditional one includes an attempt to reduce one or more reasons
that cause red eye prior to taking the picture. The second approach
is the post processing of the images to detect and then eliminate
the red-eye artifact in a post processing stage, as described in
accordance with a preferred embodiment.
[0127] There are many ways that analysis processes operating within
a camera prior to invoking a pre-flash may be configured. Various
conditions may be monitored prior to the photograph and even before
the pre-flash is generated. These conditions may include the
ambient light level and the distance of the subject from the camera
(see, e.g., U.S. Pat. No. 5,070,355 to Inoue et al., hereby
incorporated by reference). According to one embodiment, steps may
be taken that generally reduce the occurrences of a pre-flash that
may otherwise be used when warranted. In another embodiment, the
use of pre-flash is eliminated altogether. In this embodiment, the
red-eye phenomenon in a miniature camera with an integral strobe or
flash is eliminated and/or prevented without using a pre-flash,
preferably through post-processing, red-eye elimination procedures
as described above.
[0128] The use of meta-data for the post-processing of digital
images has been described above in accordance with a preferred
embodiment (see also US Publ. Pat. App. No. 2003/0058349 to
Takemoto). Meta-data contained in a digital image may be analyzed,
as may be referred to as EXIF tags, or simply tags, and utilizing
such information, global post-processing may be performed on the
image to adjust the image tone, sharpness and/or color balance.
Another way to use meta-data is in the photo-finishing industry,
where a digital image may be post-processed to optimize the output
from a printing system. Examples of this use of meta-data are
provided at U.S. Pat. Nos. 6,505,003 6,501,911 and 6,496,655 to
Mallory Desormeaux, hereby incorporated by reference. A hybrid
camera may be used which saves a copy of the original image
containing meta-data and implements a scheme which allows control
over saving the image containing metadata outside the camera. Image
meta-data may also be recorded onto a standard camera film and the
meta-data may be subsequently recovered to assist in the
post-processing of the film (see U.S. Pat. No. 6,429,924 to Milch,
hereby incorporated by reference). Advantageously in accordance
with a preferred embodiment, image meta-data may be used to
determine a size range of objects and related features within an
image, in addition to the correction of global parameters such as
image tone, sharpness and color balance.
[0129] A red-eye correction procedure may begin with detecting a
human face in a digital image and, based on this detection, finding
the eyes in the face (see, e.g., U.S. Pat. No. 6,252,976 to
Schildkraut and Gray, U.S. Publ. Pat. App. No. 2003/0044070 to
Fuersich et al., and U.S. Pat. No. 6,278,491 to Wang and Zhang,
which are incorporated by reference). This procedure may preferably
begin with detecting one or more face regions of a person or
persons in a digital image, followed by detecting an eye region or
eye regions in each face, and finally determining if red-eye
defects exist in the subject's eyes. In the '976 patent, a complex
procedure is described for detecting faces and balanced eye-pairs
from a skin-map of the image. This task involves several
partitioning and re-scaling operations. Significant additional
processing of a potential face region of the image then follows in
order to determine if a matching pair of eyes is present. Finally,
the image pixels in the detected eye regions go through a complex
scoring process to determine if a red-eye defect is present.
[0130] In a preferred process, a simplified and thus generally less
resource intensive, image processing technique is used relative to
those described at the '976 and '491 patents which detect face and
eye regions in an image and subsequently verify the presence of
red-eye defects. An advantageous technique will preferably not
weight too heavily upon detecting balanced eye pairs, as this
approach can get complex and resource intensive when two or more
facial regions overlap or are in close proximity to one another in
a digital image. According to a preferred embodiment herein,
metadata is used to simplify the detection of red-eye defects in a
digital image. For example, one or more exclusion criteria may be
employed to determine that no flash was used (see also U.S. Publ.
Pat. App. No. 2003/0044063 to Meckes et al.).
[0131] A range of alternative techniques may be employed to detect
and verify the existence of red-eye defects in an image (see, e.g.,
U.S. Publ. Pat. Apps. No. 2003/0044177 and 2003/0044178 to
Oberhardt et al., hereby incorporated by reference). A camera may
include software or firmware for automatically detecting a red-eye
image using a variety of image characteristics such as image
brightness, contrast, the presence of human skin and related
colors. The analysis of these image characteristics may be
utilized, based on certain pre-determined statistical thresholds,
to decide if red-eye defects exist and if a flash was used to take
the original image. This technique may be applied to images
captured on conventional film, which is then digitally scanned, or
to initially digitally-acquired images. Preferably, metadata is
used that can be generated by a digital camera or otherwise
recorded in or associated with the body of a digital image
initially captured or scanned. In accordance with a preferred
embodiment, meta-data an/or anthropometric data may be used to
validate the existence of a red-eye defect in an image.
[0132] Further techniques may be used alternatively to the
preferred embodiments described above for removing flash artifacts
from digital images. Two copies of a digital image may be captured,
one taken with flash illumination and a second taken without flash
illumination, and intensity histograms of the two images may be
compared in order to locate regions of the image where flash
artifacts occur and correct these by reducing intensities in these
regions (see, e.g., US Publ. Pat. App. No. 2002/0150306 to Baron).
Specular reflections may be removed due to the flash and red-eye
can be reduced in this way. However, even Baron recognizes that the
technique may involve the setting of separate thresholds for each
of the RGB image colors. A technique such as this will generally
further involve use of some additional knowledge of the captured
image if it is to be relied upon for correctly locating and
identifying red-eye defects.
[0133] Another technique may involve the identification of small
specular reflections that occur in the eye region when flash
illumination is used (see, e.g., WO 03/026278 to Jarman, which is
hereby incorporated by reference). This procedure may be used to
detect red-eye defects without first detecting a human face or eye
region. It is preferred, however, to use camera-specific
information, or other image metadata such as acquisition data, or
anthropometric data, or a combination thereof, to assist in the
confirmation of a red-eye defect.
[0134] Digital cameras can also be customized using demographic
groups (see, e.g., U.S. Publ. Pat. App. No. 2003/0025811 to Keelan
et al., hereby incorporated by reference). The rationale for this
technique is that certain aspects of image processing and the image
acquisition process such as color and tone balance may be affected
by both age-related and racial factors. It is also noted that both
racial and age factors can affect the level of red-eye defects,
which occur, and thus the pre-flash algorithms and flash-to-lens
spacing for a digital camera may be adjusted according to the
target market group based on age and nationality. Human faces may
be detected and classified according to the age of the subjects
(see, e.g., U.S. Pat. No. 5,781,650 to Lobo et al.). A number of
image processing techniques may be combined with anthropometric
data on facial features to determine an estimate of the age
category of a particular facial image. In a preferred embodiment,
the facial features and/or eye regions are validated using
anthropometric data within a digital image. The reverse approach
may also be employed and may involve a probability inference, also
known as Bayesian Statistics.
[0135] The preferred embodiments described herein may involve
expanded digital acquisition technology that inherently involves
digital cameras, but that may be integrated with other devices such
as cell-phones equipped with an acquisition component, toy cameras
etc. The digital camera or other image acquisition device of the
preferred embodiment has the capability to record not only image
data, but also additional data referred to as meta-data. The file
header of an image file, such as JPEG, TIFF, JPEG-2000, etc., may
include capture information such as whether a flash was used, the
distance as recorded by the auto-focus mechanism, the focal length
of the lens, the sensor resolution, the shutter and the aperture.
The preferred embodiments described herein serve to improve the
detection of red eyes in images, while eliminating or reducing the
occurrence of false positives, and to improve the correction of the
detected artifacts.
[0136] While an exemplary drawing and specific embodiments of the
present invention have been described and illustrated, it is to be
understood that that the scope of the present invention is not to
be limited to the particular embodiments discussed. Thus, the
embodiments shall be regarded as illustrative rather than
restrictive, and it should be understood that variations may be
made in those embodiments by workers skilled in the arts without
departing from the scope of the present invention, as set forth in
the claims below and structural and functional equivalents
thereof.
[0137] In addition, in methods that may be performed according to
preferred embodiments herein and that may have been described
above, the operations have been described in selected typographical
sequences. However, the sequences have been selected and so ordered
for typographical convenience and are not intended to imply any
particular order for performing the operations, unless expressly
set forth or understood by those skilled in the art being
necessary.
* * * * *