Face recognition base on KPCA with polynomial kernels

Author

Zhao, Li-hong ; Zhang, Xi-li ; Xu, Xin-He

Author_Institution

Northeastern Univ., Shenyang

Volume

3

fYear

2007

fDate

2-4 Nov. 2007

Firstpage

1213

Lastpage

1216

Abstract

Kernel principal component analysis (KPCA), a improving of PCA, is used in face recognition. The paper describes the use of kernel principal component analysis with polynomial kernels to extracts face image features in high-dimensional spaces. KPCA extracts feature set more suitable for categorization than classical Principal Component Analysis does. The experiments on the ORL and Yale face database demonstrate that KPCA is good at dimensional reduction, and it achieves better performance than classical Principal Component Analysis does, the highest correct recognition rate is 99%.

Keywords

face recognition; feature extraction; principal component analysis; face image feature extraction; face recognition; kernel principal component analysis; Covariance matrix; Data mining; Face recognition; Feature extraction; Image databases; Image reconstruction; Kernel; Polynomials; Principal component analysis; Spatial databases; Feature extraction; kernel PCA; polynomial kernel functions; principal components;

fLanguage

English

Publisher

ieee

Conference_Titel

Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-1065-1

Electronic_ISBN

978-1-4244-1066-8

Type

conf

DOI

10.1109/ICWAPR.2007.4421618

Filename

4421618