In this paper, we develop a new approach for gender recognition. Our approach uses the rectangle feature vector (RFV) as a representation to identify humans' gender from their faces. The RFV is computationally fast and effective to encode intensity variations of local . regions of human face. By only using few rectangle features learned by AdaBoost, we present an effective gender identifier. We then use nonlinear support vector machines for classification, and obtain more accurate identification results. Experimental results show that our approach performs well for the Feret database.
第十三屆人工智慧與應用研討會論文集=The 13th conference on artificial intelligence and applications, pp.13-17