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...

Полное описание

Библиографические подробности

Опубликовано в::	IEEE Signal processing letters 9, 2 (2002).
Главный автор:	Kwang In Kim
Формат:	Статья
Язык:	English
Предметы:	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.