Method and system for determining ethnicity category of facial images based on multi-level primary and auxiliary classifiers

Abstract

The present invention is a system and method for performing ethnicity classification based on the facial images of people, using multi-category decomposition architecture of classifiers, which include a set of predefined auxiliary classifiers that are specialized to auxiliary features of the facial images. In the multi-category decomposition architecture, which is a hybrid multi-classifier architecture specialized to ethnicity classification, the task of learning the concept of ethnicity against significant within-class variations, is handled by decomposing the set of facial images into auxiliary demographics classes; the ethnicity 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 age and gender labels as well as the ethnicity labels. Each auxiliary class machine is trained to output both the given auxiliary class membership likelihood and the ethnicity likelihoods. Faces are detected from the input image, individually tracked, and fed to all the auxiliary class machines to compute the desired auxiliary class membership and ethnicity likelihood outputs. The outputs from all the auxiliary class machines are combined in a manner to make a final decision on the ethnicity of the given face.