中文摘要：

针对核最大散度差（KMSD）方法在人脸识别中存在边缘类和次优性问题,提出一种基于核主成分分析（KPCA）与模糊最大散度差（FMSD）的人脸识别方法（KFMSD）。利用KPCA方法提取人脸的非线性结构特征,选取投影后类间散度大于类内散度的特征向量作为最优投影轴,采用FMSD方法,根据隶属度函数将样本的原始分布信息完全融入人脸的特征提取中,采用最近邻分类器进行分类识别。在ORL和YALE人脸库上的实验证明了KFMSD方法的有效性。

英文摘要：

Considering the outer classes and inferior problem in Kernel Maximum Scatter Difference（KMSD） method,a new method of face recognition based on Kernel Principal Component Analysis（KPCA） and Fuzzy Maximum Scatter Difference（FMSD） is developed.The KPCA can be benefit to develop the nonlinear structures features in faces.Selecting the eigenvectors that between-class scatter is greater than within-class scatter after projection as optimal projection axis.Distribution information of samples is represented with fuzzy membership degree in the FMSD.It uses the nearest neighbor classifier for face recognition.Experimental results on ORL and YALE face databases show the KFMSD is better than KMSD method.