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, 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.
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, 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. 9. 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.