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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Improving Face Recognition Performance Using RBPCA MaxLike and Information Fusion

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Author(s):
Salvadeo, Denis H. P. [1] ; Mascarenhas, Nelson D. A. [1] ; Moreira, Jander [1] ; Levada, Alexandre L. M. [1] ; Correa, Debora C. [2]
Total Authors: 5
Affiliation:
[1] Univ Fed Sao Carlos, Dept Comp Sci, BR-13560 Sao Carlos, SP - Brazil
[2] Phys Inst Sao Carlos, Sao Carlos, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: COMPUTING IN SCIENCE & ENGINEERING; v. 13, n. 3, p. 14-21, MAY-JUN 2011.
Web of Science Citations: 0
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. (AU)