Method and system for determining the age category of people based on facial images
First Claim
1. 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, ande) 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.
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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.
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Citations
24 Claims
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1. A method for determining age categories of people, comprising the following steps of:
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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. - View Dependent Claims (3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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2. An apparatus for determining age categories of people, comprising:
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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. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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Specification