中文摘要：

提出一种核最大间距准则方法（Kernel maximum between-class margin criterion,KMMC）的特征提取方法来避免人脸识别中的小样本问题,采用基于核特征空间的类间散度与类内散度之差的最大化的特征提取方法,获得了一组最佳鉴别矢量做为投影轴进行投影变换,使得核特征空间样本的类间散度最大,类内散度最小,从理论上解决了因类内散布矩阵奇异导致无法求解的问题,并进一步显示了KMMC特征提取的高效性.在ORL人脸库上进行试验验证,结果表明KMMC特征提取方法的有效性.最后,采用Maflab设计并实现了一种基于KMMC的人脸识别系统.

英文摘要：

This paper proposed KMMC （Kernel maximum between-class margin criterion） as the basic extraction method of face recognition to avoid small sample size problem in face recognition. Moreover, based on the maximum of the difference between between-class scatter and within-class scatter in feature space, we obtained a set of optimal discriminant vectors as the projection axis to proceed projection transformation, so that make the between-class scatter of Kernel feature space sample maximum and the within- class scatter minimum. Theoretically solved the unanswered problem caused by singularity of within-class scatter, and further demonstrates its efficiency of feature extraction. Through the test on ORL face database, the results confirmed the validity of the feature extraction method. At last, by using Matlab, we designed a face recognition system based on KMMC.