Complete Two-Dimensional PCA for Face Recognition

Author

Xu, Anbang ; Jin, Xin ; Jiang, Yugang ; Guo, Ping

Author_Institution

Image Process. & Pattern Recognition Lab., Beijing Normal Univ.

Volume

3

fYear

0

fDate

0-0 0

Firstpage

481

Lastpage

484

Abstract

We propose a novel method, the complete two-dimensional principal component analysis (complete 2DPCA), for image features extraction. Compared to the original 2DPCA, complete 2DPCA not only gain a higher recognition rate, but also reduce the feature coefficients needed for face recognition. Complete 2DPCA is based on 2D image matrices. Two image covariance matrices are constructed directly using the original image matrix and theirs eigenvectors are derived for image feature extraction. Our experiments were performed on ORL face database, and experimental results show that the proposed method has an encouraging performance

Keywords

covariance matrices; eigenvalues and eigenfunctions; face recognition; feature extraction; principal component analysis; 2D PCA; 2D image matrices; eigenvectors; face recognition; feature coefficients; image covariance matrices; image feature extraction; image matrix; principal component analysis; Covariance matrix; Face recognition; Feature extraction; Image databases; Independent component analysis; Kernel; Lighting; Pattern recognition; Principal component analysis; Scattering;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2006. ICPR 2006. 18th International Conference on

Conference_Location

Hong Kong

ISSN

1051-4651

Print_ISBN

0-7695-2521-0

Type

conf

DOI

10.1109/ICPR.2006.395

Filename

1699569