US7912246B1
Active / In Force
US
⏱️ 1.2 years remaining
Method and system for determining the age category of people based on facial images
Assignee: Videomining LLC
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Filing: 2008-01-29
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Granted: 2011-03-22
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Expires: 2028-01-29
Abstract
Abstract The present invention is a system and method for performing age classification or age estimation based on the facial images of people, using multi-category decomposition architecture of classifiers. In the multi-category decomposition architecture, which is a hybrid multi-classifier architecture specialized to age classification, the task of learning the concept of age against significant within-class variations, is handled by decomposing the set of facial images into auxiliary demographics classes, and the age classification is performed by an array of classifiers where each classifier, called an auxiliary class machine, is specialized to the given auxiliary class. The facial image data is annotated to assign the gender and ethnicity labels as well as the age labels. Each auxiliary class machine is trained to output both the given auxiliary class membership likelihood and the age group likelihoods. Faces are detected from the input image and individually tracked. Age sensitive feature vectors are extracted from the tracked faces and are fed to all of the auxiliary class machines to compute the desired likelihood outputs. The outputs from all of the auxiliary class machines are combined in a manner to make a final decision on the age of the given face.
Inventors
- 👤 Hankyu Moon
- 👤 Rajeev Sharma
- 👤 Namsoon Jung
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 11:18
CPC Classifications
G; G06; G06V; G06V10/00; G06V10/70; G06V10/74; G06V10/75; G06V10/76; G06F; G06F18/00; G06F18/20; G06F18/24; G06V10/764; G06V40/00; G06V40/10; G06V40/16; G06V40/178
Claims Analysis
Total Claims: 26
Claim 1
Independent Claim
188 words
A method for determining age categories of people, comprising the following steps of: a) annotating a facial image database according to the demographics classes of the individual face, b) training a plurality of learning machines so that each learning machine outputs auxiliary demographics class information and age information of any given facial image, c) detecting and tracking a facial image from the input image frame, d) processing said facial image to extract image features, and e) processing said image features obtained from said facial image using classification techniques for determining age or age categories, whereby the age classes can be any partition based on age in multiple groups, wherein the method further comprises a step of determining the target outputs of the plurality of learning machines so that each learning machine maps a first input data, whose facial images belong to a first auxiliary demographics class, to first vector-valued points on a manifold in the space of facial images, whereas each learning machine maps a second input data, whose facial images do not belong to a first auxiliary demographics class, to second vector-valued points away from the manifold.
Claim 2
Independent Claim
188 words
A method for determining age categories of people, comprising the following steps of: a) annotating a facial image database according to the demographics classes of the individual face, b) training a plurality of learning machines so that each learning machine outputs auxiliary demographics class information and age information of any given facial image, c) detecting and tracking a facial image from the input image frame, d) processing said facial image to extract image features, and e) processing said image features obtained from said facial image using classification techniques for determining age or age categories, whereby the age classes can be any partition based on age in multiple groups, wherein the method further comprises a step of determining the target outputs of the plurality of learning machines so that each learning machine maps a first input data, whose facial images belong to a first auxiliary demographics class, to first vector-valued points on a manifold in the space of facial images, whereas each learning machine maps a second input data, whose facial images do not belong to a first auxiliary demographics class, to second vector-valued points away from the manifold.
Claim 3
Independent Claim
193 words
An apparatus for determining age categories of people, comprising: a) means for annotating a facial image database according to the demographics classes of the individual face, b) means for training a plurality of learning machines so that each learning machine outputs auxiliary demographics class information and age information of any given facial image, c) means for detecting and tracking a facial image from the input image frame, d) means for processing said facial image to extract image features, and e) means for processing said image features obtained from said facial image using classification techniques for determining age or age categories, whereby the age classes can be any partition based on age in multiple groups, wherein the apparatus further comprises means for determining the target outputs of the plurality of learning machines so that each learning machine maps a first input data, whose facial images belong to a first auxiliary demographics class, to first vector-valued points on a manifold in the space of facial images, whereas each learning machine maps a second input data, whose facial images do not belong to a first auxiliary demographics class, to second vector-valued points away from the manifold.
Claim 4
Independent Claim
193 words
An apparatus for determining age categories of people, comprising: a) means for annotating a facial image database according to the demographics classes of the individual face, b) means for training a plurality of learning machines so that each learning machine outputs auxiliary demographics class information and age information of any given facial image, c) means for detecting and tracking a facial image from the input image frame, d) means for processing said facial image to extract image features, and e) means for processing said image features obtained from said facial image using classification techniques for determining age or age categories, whereby the age classes can be any partition based on age in multiple groups, wherein the apparatus further comprises means for determining the target outputs of the plurality of learning machines so that each learning machine maps a first input data, whose facial images belong to a first auxiliary demographics class, to first vector-valued points on a manifold in the space of facial images, whereas each learning machine maps a second input data, whose facial images do not belong to a first auxiliary demographics class, to second vector-valued points away from the manifold.
Claim 5
Dependent Claim
49 words
The method according to claim 1 , wherein the method further comprises a step of annotating the facial image database with the auxiliary class labels and the age labels of each facial image, whereby the auxiliary class is a demographics class defined by the same gender and ethnicity categories.
Claim 6
Dependent Claim
47 words
The method according to claim 1 , wherein the method further comprises a step of setting up a plurality of learning machines so that each learning machine represents an auxiliary demographics class, whereby the auxiliary demographics class comprises facial images having the same gender and ethnicity classes.
Claim 7
Dependent Claim
54 words
The method according to claim 1 , wherein the method further comprises a step of determining the target age vector outputs of the plurality of learning machines so that each learning machine outputs an age vector, wherein each component of the age vector evaluates a function that corresponds to a specific fixed age value.
Claim 8
Dependent Claim
95 words
The method according to claim 1 , wherein the method further comprises a step of training the plurality of learning machines so that each learning machine is trained to force the distance between the ground truth age vector and the age vector computed by the learning machine to have either of two target values, wherein the target value of the learning machine is zero when the input data belong to the given auxiliary class, and the target value is a fixed large value when the input data do not belong to the given auxiliary class.
Claim 9
Dependent Claim
82 words
The method according to claim 1 , wherein the method further comprises a step of projecting the age vector computed by the learning machine to the closest point on the auxiliary class manifold to determine the likelihood of an input data belonging to an auxiliary demographics class by computing the distance between the age vector and the closest point on the auxiliary class manifold, and to directly use the closest point on the auxiliary manifold as information relevant to determine the age.
Claim 10
Dependent Claim
66 words
The method according to claim 1 , wherein the method further comprises a step of combining the auxiliary class information and the age information from the plurality of learning machines to estimate the age or age group of the input face, so that the degree of contribution of the age information from each learning machine is determined by the auxiliary class information from the same machine.
Claim 11
Dependent Claim
104 words
The method according to claim 1 , wherein the method further comprises a step of extracting a set of age sensitive features from a facial image, wherein the set of age sensitive features is fed to auxiliary demographics class machines, wherein each of the auxiliary demographics class machines computes an auxiliary class likelihood output of the facial image belonging to the auxiliary demographics class and age outputs, and whereby the set of age sensitive features are computed by applying a set of high frequency filters that are designed to extract the positions and shapes of facial boundary, facial features, and to extract facial wrinkles.
Claim 12
Dependent Claim
66 words
The method according to claim 9 , wherein the method further comprises a step of training a plurality of learning machines using the age sensitive features as training data to classify the age of the facial image, wherein a desired age vector and auxiliary class likelihood output is calculated for each training data, and wherein each training data corresponds to one of the auxiliary demographics classes.
Claim 13
Dependent Claim
36 words
The method according to claim 1 , wherein the method further comprises a step of arranging the plurality of learning machines in serial, wherein the arrangement can be any sequential ordering of the plurality of classifiers.
Claim 14
Dependent Claim
23 words
The method according to claim 1 , wherein the method further comprises a step of arranging the plurality of learning machines in parallel.
Claim 15
Dependent Claim
84 words
The method according to claim 1 , wherein the method further comprises a step of arranging a plurality of classifiers in a manner of combining chains of serial classifiers in parallel, whereby the first classifier in each chain of serial classifiers performs a gross-level classification into gross-level classes by computing gross-level class information and the second classifier in each chain of serial classifiers performs a finer level classification that is specialized to one of the gross-level classes by computing the finer level class information.
Claim 16
Dependent Claim
48 words
The apparatus according to claim 2 , wherein the apparatus further comprises means for annotating the facial image database with the auxiliary class labels and the age labels of each facial image, whereby the auxiliary class is a demographics class defined by the same gender and ethnicity categories.
Claim 17
Dependent Claim
46 words
The apparatus according to claim 2 , wherein the apparatus further comprises means for setting up a plurality of learning machines so that each learning machine represents an auxiliary demographics class, whereby the auxiliary demographics class comprises facial images having the same gender and ethnicity classes.
Claim 18
Dependent Claim
53 words
The apparatus according to claim 2 , wherein the apparatus further comprises means for determining the target age vector outputs of the plurality of learning machines so that each learning machine outputs an age vector, wherein each component of the age vector evaluates a function that corresponds to a specific fixed age value.
Claim 19
Dependent Claim
94 words
The apparatus according to claim 2 , wherein the apparatus further comprises means for training the plurality of learning machines so that each learning machine is trained to force the distance between the ground truth age vector and the age vector computed by the learning machine to have either of two target values, wherein the target value of the learning machine is zero when the input data belong to the given auxiliary class, and the target value is a fixed large value when the input data do not belong to the given auxiliary class.
Claim 20
Dependent Claim
81 words
The apparatus according to claim 2 , wherein the apparatus further comprises means for projecting the age vector computed by the learning machine to the closest point on the auxiliary class manifold to determine the likelihood of an input data belonging to an auxiliary demographics class by computing the distance between the age vector and the closest point on the auxiliary class manifold, and to directly use the closest point on the auxiliary manifold as information relevant to determine the age.
Claim 21
Dependent Claim
65 words
The apparatus according to claim 2 , wherein the apparatus further comprises means for combining the auxiliary class information and the age information from the plurality of learning machines to estimate the age or age group of the input face, so that the degree of contribution of the age information from each learning machine is determined by the auxiliary class information from the same machine.
Claim 22
Dependent Claim
103 words
The apparatus according to claim 2 , wherein the apparatus further comprises means for extracting a set of age sensitive features from a facial image, wherein the set of age sensitive features is fed to auxiliary demographics class machines, wherein each of the auxiliary demographics class machines computes an auxiliary class likelihood output of the facial image belonging to the auxiliary demographics class and age outputs, and whereby the set of age sensitive features are computed by applying a set of high frequency filters that are designed to extract the positions and shapes of facial boundary, facial features, and to extract facial wrinkles.
Claim 23
Dependent Claim
65 words
The apparatus according to claim 20 , wherein the apparatus further comprises means for training a plurality of learning machines using the age sensitive features as training data to classify the age of the facial image, wherein a desired age vector and auxiliary class likelihood output is calculated for each training data, and wherein each training data corresponds to one of the auxiliary demographics classes.
Claim 24
Dependent Claim
35 words
The apparatus according to claim 2 , wherein the apparatus further comprises means for arranging the plurality of learning machines in serial, wherein the arrangement can be any sequential ordering of the plurality of classifiers.
Claim 25
Dependent Claim
22 words
The apparatus according to claim 2 , wherein the apparatus further comprises means for arranging the plurality of learning machines in parallel.
Claim 26
Dependent Claim
83 words
The apparatus according to claim 2 , wherein the apparatus further comprises means for arranging a plurality of classifiers in a manner of combining chains of serial classifiers in parallel, whereby the first classifier in each chain of serial classifiers performs a gross-level classification into gross-level classes by computing gross-level class information and the second classifier in each chain of serial classifiers performs a finer level classification that is specialized to one of the gross-level classes by computing the finer level class information.
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