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Improving Face Recognition Performance Using RBPCA MaxLike and Information Fusion
May/June 2011 (vol. 13 no. 3)
pp. 14-21
Denis Salvadeo, Federal University of São Carlos, Brazil
Nelson Mascarenhas, Federal University of São Carlos, Brazil
Jander Moreira, Federal University of São Carlos, Brazil
Alexandre Levada, Federal University of São Carlos, Brazil
Débora C. Corrêa, Physics Institute of São Carlos, Brazil

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.

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Index Terms:
Face recognition, principal component analysis, block-based PCA, covariance matrix regularization, maximum likelihood, multiclassifier fusion, security and privacy, scientific computing
Citation:
Denis Salvadeo, Nelson Mascarenhas, Jander Moreira, Alexandre Levada, Débora C. Corrêa, "Improving Face Recognition Performance Using RBPCA MaxLike and Information Fusion," Computing in Science and Engineering, vol. 13, no. 3, pp. 14-21, May-June 2011, doi:10.1109/MCSE.2010.142
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