An improved Bayesian face recognition algorithm in PCA subspace

Author

Wang, Xiuogang ; Tang, Xiaoou

Author_Institution

Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Shatin, China

Volume

3

fYear

2003

fDate

6-10 April 2003

Abstract

Through modeling the difference between two face images by three components, intrinsic difference (I), transformation difference (T), and random noise (N), we show that the Bayesian algorithm can successfully separate the main disturbing component, T, from the discriminating component, I, however, at a cost of magnified noise, N. To control the noise, we apply PCA on the original image, then carry out the Bayesian analysis in the reduced PCA space. The new method is shown to be more effective than the standard Bayesian algorithm in experiments using 2000+ face images from the Feret database.

Keywords

Bayes methods; eigenvalues and eigenfunctions; face recognition; principal component analysis; random noise; Bayesian algorithm; Bayesian face recognition algorithm; PCA subspace; discriminating component; disturbing component; eigenvalues; intrinsic difference; principal component analysis; random noise; transformation difference; Algorithm design and analysis; Artificial intelligence; Bayesian methods; Costs; Eigenvalues and eigenfunctions; Face recognition; Image analysis; Information analysis; Noise reduction; Principal component analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on

ISSN

1520-6149

Print_ISBN

0-7803-7663-3

Type

conf

DOI

10.1109/ICASSP.2003.1199124

Filename

1199124