Face recognition with supervised spectral regression and multiple kernel SVM

Author

Xiao, Yongliang ; Xia, Limin ; Zhang, Wei

Author_Institution

Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China

Volume

4

fYear

2010

fDate

27-29 March 2010

Firstpage

343

Lastpage

346

Abstract

Face recognition plays an every important role in security surveillance, secure access and identity authentication. In this paper, we propose a novel face recognition method based on supervised learning. Our method consists in first extracting face feature using a supervised spectral regression, then we use multiple kernel SVM to classify face. Experimental results on Yale B face database and AR face database show the effectiveness of the proposed method.

Keywords

face recognition; learning (artificial intelligence); regression analysis; spectral analysis; support vector machines; visual databases; AR face database; Yale B face database; face recognition method; identity authentication; multiple kernel SVM; security surveillance; supervised learning; supervised spectral regression; Authentication; Data security; Face recognition; Feature extraction; Kernel; Spatial databases; Supervised learning; Support vector machine classification; Support vector machines; Surveillance; SVM; face recognition; multiple kernel learnin; spectral regression; supervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computer Control (ICACC), 2010 2nd International Conference on

Conference_Location

Shenyang

Print_ISBN

978-1-4244-5845-5

Type

conf

DOI

10.1109/ICACC.2010.5486908

Filename

5486908