基于核的Foley-Sammon鉴别分析与人脸识别

通过建立基于核的Foley-Sammon鉴别分析(KFSDA)的两个等价模型，并分析这两个等价模型的解之间的关系，从理论上给出KFSDA模型的具体求解方法．分析表明，基于核的Foley-Sammon鉴别分析保留了FSDA能明显降低样本特征之间冗余信息的优点，更重要的是该方法能够有效地抽取样本的非线性特征，是对FSDA的进一步拓展．在ORL标准人脸库上的实验结果验证了文中方法的有效性．

Kernel- Based Foley-Sammon Discriminant Analysis and Face Recognition

Through implementing two equivalent models of kernel based Foley Sammon discriminant analysis (KFSDA) and studying the relationship between the solutions of these two models, a new approach of solving the KFSDA model is presented and proved Analysis shows that KFSDA retains FSDA's advantage of distinctly reducing the redundant information among components of the pattern samples, and more importantly, it can extract nonlinear features effectively, thus greatly enhancing the capability of FSDA Experimental results on ORL face database indicate that the proposed method is valid