Improving Face Recognition Performance Using RBPCA MaxLike and Information Fusion

Author

Salvadeo, Denis H P ; Mascarenhas, Nelson D A ; Moreira, Jander ; Levada, Alexandre L M ; Corrêa, Débora C.

Author_Institution

Fed. Univ. of Sao Carlos, São Carlos, Brazil

Volume

13

Issue

3

fYear

2011

Firstpage

14

Lastpage

21

Abstract

Face recognition is typically an ill-posed problem because of the limited number of available samples. As experimental results show, combining multiclassifier fusion with the RBPCA MaxLike approach, which couples covariance matrix regularization and block-based principal component analysis (BPCA), can provide an effective framework for face recognition that alleviates the small sample size problem.

Keywords

covariance matrices; face recognition; image classification; image fusion; principal component analysis; RBPCA MaxLike approach; block-based principal component analysis; covariance matrix regularization; face recognition; information fusion; multiclassifier fusion; Artificial neural networks; Covariance matrix; Databases; Face recognition; Feature extraction; Principal component analysis; Face recognition; block-based PCA; covariance matrix regularization; maximum likelihood; multiclassifier fusion; principal component analysis; scientific computing; security and privacy;

fLanguage

English

Journal_Title

Computing in Science & Engineering

Publisher

ieee

ISSN

1521-9615

Type

jour

DOI

10.1109/MCSE.2010.142

Filename

5654484