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Gender classification of human faces using hybrid classifier systems

This paper considers a hybrid classification architectures for gender classification of human faces and shows its feasibility using a collection of 2000 face images from the FERET database (corresponding to 700 male and 300 female subjects). The hybrid approach consists of an ensemble of RBF networks and inductive decision trees (DT). Specifically cross validation (CV) experimental results yield an average accuracy rate of 94% for the hybrid architecture consisting of ensemble of RBF networks (Model 2) and decision trees ('C4.5'). The benefits of our hybrid architecture, beyond the high accuracy achieved, 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 DT.