Face recognition based on committee machine

Author

Gan, Jun-Ying ; He, Si-Bin

Author_Institution

Sch. of Inf., WuYi Univ., Jiangmen

Volume

1

fYear

2008

fDate

30-31 Aug. 2008

Firstpage

352

Lastpage

355

Abstract

Face recognition has been of interest to a growing number of researchers, and many algorithms are presented. However, the recognition rate will be significantly reduced in the case of large sample size and greater facial expression changes. In this paper, 2DPCA algorithm is used for features extraction and Boosting by filtering method is used to choose training samples. Then, the expert systems of Committee Machine are constructed and applied in face recognition. Experimental results based on ORL face database and Yale face database show that the recognition rate can be improved effectively.

Keywords

face recognition; feature extraction; filtering theory; principal component analysis; 2DPCA algorithm; Boosting; committee machine; expert systems; face database; face recognition rate; facial expression changes; feature extraction; filtering method; principal component analysis; Boosting; Covariance matrix; Face recognition; Feature extraction; Filtering algorithms; Image databases; Pattern analysis; Pattern recognition; Spatial databases; Wavelet analysis; Boosting by filtering; Committee Machine; Two-dimensional PCA;

fLanguage

English

Publisher

ieee

Conference_Titel

Wavelet Analysis and Pattern Recognition, 2008. ICWAPR '08. International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4244-2238-8

Electronic_ISBN

978-1-4244-2239-5

Type

conf

DOI

10.1109/ICWAPR.2008.4635803

Filename

4635803