关键词: 核判别分析, 核主成分分析, 广义最近特征线, 人脸识别

Abstract: Kernel Discriminant Anlaysis (KDA) and Kernel Principal Component Analysis (KPCA) are the nonlinear extensions of Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) respectively. In this paper, we presented a feature extraction algorithm by combing KDA and KPCA to extract reliable and robust features for recognition. Furthermore, a generalized nearest feature line (GNFL) method was also presented for constructing powerful classifier. The performance of the proposed method was demonstrated through real data.

Key words: Kernel Discriminant Analysis (KDA), Kernel Principal Component Analysis (KPCA), Generalized Nearest Feature Line (GNFL), face recognition