A framework for multi-view gender classification

Abstract

This paper proposes a novel framework for dealing with multi-view gender classification problems and shows its feasibility on the CAS-PEAL database of face images. The framework consists of three stages. First, wavelet transform is used to intensify multi-scale edges and remove effects of illumination and noises. Second, instead of traditional Euclidean distance, image Euclidean distance which considers the spatial relationships between pixels is used to measure the distance between images. Last, a two layer support vector machine is proposed, which divides face images into different poses in the first layer, and then recognizes the gender with different support vector machines in the second layer. Compared with traditional support vector machines and min-max modular network with support vector machines, our method achieves higher classification accuracy and spends less training and test time. © 2008 Springer-Verlag Berlin Heidelberg.