Face recognition method based on Within-class Clustering SVM

Author

Wu, Yan ; Yao, Xiao ; Xia, Ying

Author_Institution

Dept. of Comput. Sci. & Eng., Tongji Univ., Shanghai

fYear

2008

fDate

21-24 Sept. 2008

Firstpage

370

Lastpage

374

Abstract

A face recognition method based on within-class Clustering SVM (CCSVM) is presented in this paper in order to decrease the negative effect caused by noisy training samples in the recognition process. Based on the discontinuity of finite samples distribution in the high dimension space and the existence of noisy samples, first, we re-cluster samples within the class, find out the cluster centres to form the virtual classes, and then divide virtual classes of all the classes by SVM. Experiment results show that this method follows the distribution law of points in high-dimensional space and can achieve better performance than some traditional methods.

Keywords

face recognition; pattern clustering; support vector machines; SVM; face recognition method; noisy samples; support vector machine; within-class clustering; Biometrics; Cause effect analysis; Clustering algorithms; Computer science; Corporate acquisitions; Face recognition; Merging; Pattern recognition; Support vector machine classification; Support vector machines; face recognition; support vector machine (SVM); within-class cluster;

fLanguage

English

Publisher

ieee

Conference_Titel

Cybernetics and Intelligent Systems, 2008 IEEE Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-1673-8

Electronic_ISBN

978-1-4244-1674-5

Type

conf

DOI

10.1109/ICCIS.2008.4670830

Filename

4670830