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Face recognition using kernel principal component analysis.

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 me...

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Vydáno v:IEEE Signal processing letters 9, 2 (2002).
Hlavní autor: Kwang In Kim
Médium: Článek
Jazyk:angličtina
Témata:
Face feature extraction.
Face recognition.
Facial image.
Feature space.
High-order correlations.
Input pixels.
Input space.
Kernel PCA.
Kernel principal component analysis.
Nonlinear mapping.
Polynomial kernel.