Cross-Database Evaluation of Normalized Raw Pixels for Gender Recognition under Unconstrained Settings

This paper presents cross-database evaluations of automatic appearance-based gender recognition methodology using normalized raw pixels and SVM classifier under unconstrained settings. Proposed method uses both histogram specification and feature space normalization on automatically aligned faces to achieve reliable recognition rate for real scenarios. Using a web based unconstrained training database, we applied local window search to increase generalization ability of the proposed method. Our contribution is two-fold. First we showed that aligned and normalized raw pixel intensities are providing the best performance in case of unconstrained cross-database tests than feature-based studies on unaligned faces. Second, we showed that histogram specification provides better normalization than that of histogram equalization for automatically aligned faces in large databases for gender recognition. Variety of cross-database experiments performed on uncontrolled Image of Groups (88.16%), Genki-4K (91.07%) and LFW databases (91.87%) showed that proposed method provides superior generalization ability than that of the state-of-the-art methods.