关键词: 核独立成分分析, 独立成分分析, 广义核方差, 人脸识别

Abstract: Independent component analysis (ICA), mainly based on single linear functions, is an approach widely used in face recognition. A new approach to face recognition—kernel independent component analysis(KICA) was introduced that was based on an entire function space of nonlinear subspace. First introduced ICA in a concise way and mainly discuss the KICA’s basic principle and algorithm, and analyzed the differences between the ICA and KICA in face recognition. Finally, the experimental and analytical results show that in face recognition KICA algorithm outperforms ICA algorithm.

Key words: kernel independent component analysis (KICA), independent component analysis (ICA), kernel generalized variance (KGV), face recognition