An adaptive nonparametric discriminant analysis method and its application to face recognition

作者：Huang, Liang^*; Ma, Yong; Ijiri, Yoshihisa; Lao, Shihong; Kawade, Masato; Zhao, Yuming

来源：8th Asian Conference on Computer Vision, Japan, 2007-11-18 to 2007-11-22.

• linear discriminant analysis
• nearest neighbors
• nonparametric discriminant analysis
• face recognition

Linear Discriminant Analysis (LDA) is frequently used for dimension reduction and has been successfully utilized in many applications, especially face recognition. In classical LDA, however, the definition of the between-class scatter matrix can cause large overlaps between neighboring classes, because LDA assumes that all classes obey a Gaussian distribution with the same covariance. We therefore, propose an adaptive nonparametric discriminant analysis (ANDA) algorithm that maximizes the distance between neighboring samples belonging to different classes, thus improving the discriminating power of the samples near the classification borders. To evaluate its performance thoroughly, we have compared our ANDA algorithm with traditional PCA LDA, Orthogonal LIDA (OLDA) and nonparametric discriminant analysis (NDA) on the FERET and ORL face databases. Experimental results show that the proposed algorithm outperforms the others.

• 出版日期2007
• 单位上海交通大学