Gender classification using faces and gaits

Abstract

Gender classification is one of the challenging problems in computer vision. Many interactive applications need to exactly recognize human genders. In this paper, we are carrying out some experiments to classify the human gender in conditions of low captured video resolution. We use Local Binary Pattern, Gray Level Co-occurrence Matrix to extract the features from faces and Gait Energy Motion, Gait Energy Image for gaits. We propose to combine face and gait features with the combination classifier to enhance gender classification performance. © 2013 Springer Science+Business Media Dordrecht(Outside the USA).