Face recognition using kernel principal component analysis

Kim, Kwang In and Jung, Keechul and Kim, Hang Joon (2002) Face recognition using kernel principal component analysis. IEEE Signal Processing Letters, 9 (2). pp. 40-42. ISSN 1070-9908

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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.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Signal Processing Letters
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
69825
Deposited By:
Deposited On:
02 Jul 2014 10:33
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Oct 2020 02:31