Studies on human face recognition based on greedy kernel principal component analysis

Author

Xiaozhe Wang ; Jinping Wang ; Chenyang Li

Author_Institution

Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China

fYear

2012

fDate

23-25 May 2012

Firstpage

1446

Lastpage

1449

Abstract

A human face recognition algorithm based on greedy kernel principal component analysis (GKPCA) is presented to meet the requirement of quick face recognition on line. In the algorithm, typical human face are decomposed by fast wavelet transform(FWT), then the greedy algorithm is used to reduce training set and the features of the low frequency sub-images are extracted by kernel principal component analysis(KPCA). Consequently, the features extracted are recognized by support vector machine (SVM). Simulations of the algorithm proposed on the basis of ORL (Olivetti Research Lab) face database and NORL face databases show that the algorithm is capable of reducing training time with high recognition rate.

Keywords

face recognition; feature extraction; greedy algorithms; principal component analysis; support vector machines; wavelet transforms; FWT; GKPCA; NORL face database; ORL face database; Olivetti Research Lab face database; SVM; fast wavelet transform; feature extraction; greedy kernel principal component analysis; human face recognition; low-frequency subimages; recognition rate; support vector machine; training set reduction; training time reduction; Face; Face recognition; Feature extraction; Kernel; Support vector machines; Training; Vectors; Face Recognition; Fast Wavelet Transform (FWT); Greedy Algorithm; Kernel Principal Component Analysis (KPCA); Support Vector Machine (SVM);

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2012 24th Chinese

Conference_Location

Taiyuan

Print_ISBN

978-1-4577-2073-4

Type

conf

DOI

10.1109/CCDC.2012.6244231

Filename

6244231