Face Recognition Based on Kernel Fisher Nonlinear Optimal Discriminant Analysis
基于核的Fisher非线性最佳鉴别分析在人脸识别中的应用

Extracting the most discriminatory features is important in face recognition tasks. In the case of a small number of face samples, as the existed methods for extracting nonlinear most discriminatory face features encounter various problems. So a new method for extracting fisher nonlinear most discriminatory features is proposed in this paper. The fisher criterion is formulated using between-class scatter matrix and within-class scatter matrix based on kernel method. Thus nonlinear most discriminatory features are obtained. However, this method causes ill-problem. To solve this problem, we search optimal discriminant vectors in null space of within-class scatter matrix. Repeated experimental results on ORL database indicate that the proposed method significantly outperforms the Fisher linear discriminant analysis(FLDA) and generalized discriminant analysis(GDA).