Face Recognition Using Kernel Methods

Abstract

Principal Component Analysis and Fisher Linear Discriminant methods have demonstrated their success in face detection, recog(cid:173) nition, and tracking. The representation in these subspace methods is based on second order statistics of the image set, and does not address higher order statistical dependencies such as the relation(cid:173) ships among three or more pixels. Recently Higher Order Statistics and Independent Component Analysis (ICA) have been used as in(cid:173) formative low dimensional representations for visual recognition. In this paper, we investigate the use of Kernel Principal Compo(cid:173) nent Analysis and Kernel Fisher Linear Discriminant for learning low dimensional representations for face recognition, which we call Kernel Eigenface and Kernel Fisherface methods. While Eigenface and Fisherface methods aim to find projection directions based on the second order correlation of samples, Kernel Eigenface and Ker(cid:173) nel Fisherface methods provide generalizations which take higher order correlations into account. We compare the performance of kernel methods with Eigenface, Fisherface and ICA-based meth(cid:173) ods for face recognition with variation in pose, scale, lighting and expression. Experimental results show that kernel methods pro(cid:173) vide better representations and achieve lower error rates for face recognition.