Face recognition using kernel principal component analysis 论文

详细信息

发表期刊/会议

IEEE Signal Processing Letters

发表日期

2002-02-01

发表年份

2002

摘要

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.