Feature extraction of face images using kernel approach

Fisher discriminant methods (FDM) have been demonstrated their success in face recognition, detection, and tracking. Fisher discriminant method is based on the optimum of Fisher discriminant criterion. Recently Higher Order Statistics (HOS) has been applied to many pattern recognition problems. In this paper we investigate a generalization of FDM, Kernel Fisher discriminant methods (KFDM), for the feature extraction of face images, which is nonlinear analysis method. In conventional FDM, all the matrices including within-class scatter matrix, between-class scatter matrix and population scatter matrix are actually a second order correlation of patterns respectively, KFDM provides a replacement which takes into account of higher order correlation. Furthermore, KFDM computes the higher order statistics without the combinatorial explosion of time and memory complexity. We compare the recognition results using KFDM with conventional FDM on ORL face image database. Experimental results show that the proposed KFDM outperforms conventional FDM in face recognition