Face recognition method by fusion local singular value features
融合局部奇异值特性的人脸识别方法*

This paper presented a face recognition method using singular value decomposition (SVD) on human local facial area and gray correlation analysis to solve the problem that it could not provide enough information for face recognition by the method of using singular value decomposition on whole facial image. The key of this approach was that applied SVD to different parts of facial area instead of the whole facial region. So the rich information could be obtained and the problem of small sample size could be solved. In the recognition step, set up the features vector of input facial image, and then computed the membership degrees of these features to each facial sample respectively, and finally obtained the decision. Comparative experimental results on ORL and AR face database show that the performance is better than that of tradition SVD-based method and robustness to the change of impression and occlusion.