Gender and ethnic classification of human faces using hybrid classifiers

Abstract

This paper considers hybrid classification architectures for gender and ethnic classification of human faces and shows their feasibility using a collection of 3006 face images corresponding to 1009 subjects from the FERET database. The hybrid approach consists of an ensemble of RBF networks and inductive decision trees (DT). Experimental cross validation (CV) results yield an average accuracy rate of (a) 96% on the gender classification task and (b) 94% on the ethnic classification task. The benefits of our hybrid architecture include: (i) robustness via query by consensus provided by the ensembles of RBF networks, and (ii) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds provided by using only DT.