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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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.

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Journal Article
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IEEE Signal Processing Letters
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02 Jul 2014 10:33
Last Modified:
06 Dec 2021 03:08