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2D Principal Component Analysis for Face and Facial-Expression Recognition
May/June 2011 (vol. 13 no. 3)
pp. 9-13
Luiz Oliveira, UFPR, Curitiba
Marcelo Mansano, UEPG, Ponta Grossa
Alessandro Koerich, PUCPR, Curitiba
Alceu de Souza Britto, Jr., PUCPR Pontifical Catholic University of Parana, Curitiba Curitiba

Although it shows enormous potential as a feature extractor, 2D principal component analysis produces numerous coefficients. Using a feature-selection algorithm based on a multiobjective genetic algorithm to analyze and discard irrelevant coefficients offers a solution that considerably reduces the number of coefficients, while also improving recognition rates.

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Index Terms:
Face recognition, facial expression recognition, feature selection, scientific computing, graphics and multimedia
Citation:
Luiz Oliveira, Marcelo Mansano, Alessandro Koerich, Alceu de Souza Britto, Jr., "2D Principal Component Analysis for Face and Facial-Expression Recognition," Computing in Science and Engineering, vol. 13, no. 3, pp. 9-13, May-June 2011, doi:10.1109/MCSE.2010.149
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