Gender classification via gradientfaces

Abstract

In this paper illumination invariant, pose and facial expression tolerant gender classification method is proposed. A recently introduced feature extraction method, namely Gradientfaces, is utilized together with Support Vector Machine (SVM) as a classifier. Image regions obtained from cascaded Adaboost based face detector is used at the feature extraction step and faster classification is achieved by using only 20-by-20 pixel region during feature extraction. For performance evaluation, two well-known face databases, FERET and Yale B are tested and the algorithm is compared against a pixelbased algorithm on these datasets. The results indicate that Gradientfaces significantly outperform the pixel-based methods under severe illumination, pose and facial expression variances. © 2011 Springer Science+Business Media B.V.