IEEE SIGNAL PROCESSING LETTERS, v.9, no.2, pp.40 - 42
Abstract
A kernel principal component analysis (PCA) was recently proposed as a nonlinear extension of a PCA. The basic idea is to first map the input space into a feature space via nonlinear mapping and then compute the principal components in that feature space. This letter adopts the kernel PCA as a mechanism for extracting facial features. Through adopting a polynomial kernel, the principal components can be computed within the space spanned by high-order correlations of input pixels making up a facial image, thereby producing a good performance.