Face Recognition Using Kernel Maximum Scatter Difference Discriminant Analysis
一种核最大散度差判别分析人脸识别方法

An efficient nonlinear subspace learning method, kernel maximum scatter difference discriminant analysis (KMSI),was proposed for face recognition in this paper. The main idea of KMSI)is to map the input sample data into feature space by nonlinear function, and then adopt maximum scatter difference discriminant analysis(MSD) to find the solution in feature space by kernel trick. The experimental results on the Yale and ORL face image database show that the proposed KMSI)method for face recognition has higher recognition rate and more effective.