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）, 判别公共矢量（DCVs）, 公共矢量（CVs）, 人脸识别

Abstract: In this paper，the Minimum Class Variance Support Vector Machines Based on Common Vectors（CV-MCVSVM） as the improved version of Minimum Class Variance Support Vector Machines（MCVSVM） is presented for noisy face recognition，which inherites the advantages of MCVSVM. Scom which constituted by the Common Vectors（CVs） is introduced.CVs contain the common information in the samples，so CV-MCVSVM utilizes the mean value of CVs to retain more information on the classification and improve the performance of noisy face classification.The experimental results about noisy face classification demonstrate that the proposed CV-MCVSVM has better classification performance than both MCVSVMs and TM-v-SVM.

Key words: Support Vector Machine（SVM）, Minimum Class Variance Support Machines（MCVSVMs）, Total Margin v Support Vector Machine（TM-v-SVM）, Discriminative Common Vectors（DCVs）, Common Vectors（CVs）, face recognition