Abstract
In this paper, we have reported a new face image classification algorithm based on Renyi entropy component analysis. In the proposed model, kernel discriminant analysis is integrated with entropy analysis to choose the best principal component vectors which 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 kernel discriminative embedding technique and comparative analysis is made with conventional kernel linear discriminant method to signify the importance of selection of principal component vectors based on entropy information rather based only on magnitude of eigenvalues.
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M., S.K., B.H., S. (2011). Kernel Discriminative Embedding Technique with Entropy Component Analysis for Accurate Face Image Classification. In: Venugopal, K.R., Patnaik, L.M. (eds) Computer Networks and Intelligent Computing. ICIP 2011. Communications in Computer and Information Science, vol 157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22786-8_22
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DOI: https://doi.org/10.1007/978-3-642-22786-8_22
Publisher Name: Springer, Berlin, Heidelberg
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