Letters: Face recognition using kernel entropy component analysis

Authors:
B. H. Shekar;M. Sharmila Kumari;Leonid M. Mestetskiy;Natalia F. Dyshkant
Affiliations:
Department of Computer Science, Mangalore University, Mangalore, Karnataka, India;Department of Computer Science & Engineering, PA College of Engineering, Mangalore, Karnataka, India;Department of Computational Mathematics and Cybernetics, Moscow State University, Moscow, Russia;Department of Computational Mathematics and Cybernetics, Moscow State University, Moscow, Russia
Venue:
Neurocomputing
Year:
2011

Citing 5
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Abstract

In this letter, we have reported a new face recognition algorithm based on Renyi entropy component analysis. In the proposed model, kernel-based methodology is integrated with entropy analysis to choose the best principal component vectors that are subsequently used for pattern projection to a lower-dimensional space. Extensive experimentation on Yale and UMIST face database has been conducted to reveal the performance of the entropy based principal component analysis method and comparative analysis is made with the kernel principal component analysis method to signify the importance of selection of principal component vectors based on entropy information rather based only on magnitude of eigenvalues.