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Improved Bayesian Approach for Face Recognition

Jiang, X-D; Mandal, B; Kot, A


X-D Jiang

A Kot


In subspace face recognition, PCA, LDA and Bayesian are the most commonly used methods. Each of them has their own advantages and disadvantages in recognizing human faces. Their recognition rates depend much on the methodologies used in selecting/transforming the eigenvectors using the eigenvalues obtained from the face subspaces. In this paper we compare all these three methods and propose a new methodology for selecting the eigenvalues in the face subspace which can be used for measuring the residual reconstruction error in the partial Karhunen-Loeve Transformation (KLT) for Bayesian face recognition. We compare the recognition performances of all these methods on FERET image database on various image sizes. Experimental results using a large set of faces - 2388 images drawn from 1194 subjects separated into training, gallery and probe datasets show that our proposed method consistently improves the performance over the Bayesian, LDA and PCA approaches.

Conference Name International Conference on Information, Communications and Signal Processing
Conference Location Bangkok, Thailand
Start Date Dec 6, 2005
End Date Dec 9, 2005
Acceptance Date Sep 7, 2005
Publication Date Dec 1, 2005
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Pages 162-166
Series Title IEEE Fifth International Conference on Information, Communications and Signal Processing (ICICS 2005)
Book Title 2005 5th International Conference on Information Communications & Signal Processing, Bangkok, 2005
ISBN 0-7803-9283-3
Keywords Bayesian methods, face recognition, principal component analysis, linear discriminant analysis, Eigenvalues and Eigenfunctions, humans, image reconstruction, Karhunen-Loeve transforms, Image recognition, Image databases
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