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Face recognition using kernel principal component analysis

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<mark>Journal publication date</mark>2002
<mark>Journal</mark>IEEE Signal Processing Letters
Issue number2
Volume9
Number of pages3
Pages (from-to)40-42
Publication statusPublished
Original languageEnglish

Abstract

A kernel principal component analysis (PCA) was previously 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 article 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.