A new kernel Fisher discriminant algorithm with application to face recognition

Kernel-based methods have been of wide concern in the ,eld of machine learning and neurocomputing. In this paper, a new Kernel Fisher discriminant analysis (KFD) algorithm, called complete KFD (CKFD), is developed. CKFD has two advantages over the existing KFD algorithms. First, its implementation is divided into two phases, i.e., Kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (FLD), which makes it more transparent and simpler. Second, CKFD can make use of two categories of discriminant information, which makes it more powerful. The proposed algorithm was applied to face recognition and tested on a subset of the FERET database. The experimental results demonstrate that CKFD is signi,cantly better than the algorithms of Kernel Fisherface and Kernel Eigenface. c © 2003 Elsevier B.V. All rights reserved.