关键词: 支持向量机（SVM）, 最小类内方差支持向量机（MCVSVMs）, 总间隔v-支持向量机（TM-v-SVM）, 人脸识别, 主成分分析（PCA）, 核主成分分析（KPCA）

Abstract: In this paper，the Total Margin[v]Minimum Class Variance Support Vector Machines（TM-[v]-MCVSVMs） as the improved version of Minimum Class Variance Support Vector Machines（MCVSVMs） is presented for noisy face recognition，which integrates the advantages of MCVSVMs with TM-[v]-SVM.The discussions about the proposed TM-[v]-MCVSVMs for “small sample size” problem and nonlinear classifications are also given.The experimental results about noisy face classification demonstrate that the proposed TM-v-MCVSVMs has better classification performance than both MCVSVMs and TM-v-SVM.

Key words: Support Vector Machine（SVM）, Minimum Class Variance Support Vector Machines（MCVSVMs）, Total Margin vsupport Vector Machine（TM-v-SVM）, face recognition, Principal Component Analysis（PCA）, Kernel Principal Component Analysis（KPCA）