高技术通讯（英文版）2002，Vol.8Issue(4)：43-46,4.
Face Recognition Using Kernel Discriminant Analysis
Face Recognition Using Kernel Discriminant Analysis
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作者信息
- 1. The Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, P.R. China;The Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, P.R. China;The Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, P.R. China
- 折叠
摘要
Abstract
Linear Discrimiant Analysis (LDA) has demonstrated their success in face recognition. But LDA is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination in face recognition. In order to overcome these problems, we investigate Kernel Discriminant Analysis (KDA) for face recognition. This approach adopts the kernel functions to replace the dot products of nonlinear mapping in the high dimensional feature space, and then the nonlinear problem can be solved in the input space conveniently without explicit mapping. Two face databases are used to test KDA approach. The results show that our approach outperforms the conventional PCA(Eigenface) and LDA(Fisherface) approaches.关键词
face recognition/linear discriminant analysis/kernel discriminant analysisKey words
face recognition/linear discriminant analysis/kernel discriminant analysis分类
信息技术与安全科学引用本文复制引用
..Face Recognition Using Kernel Discriminant Analysis[J].高技术通讯（英文版）,2002,8(4):43-46,4.
