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ABSTRACT
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.
INDEX TERMS
face recognition, feature extraction, genetic algorithms, principal component analysis
CITATION
"2D Principal Component Analysis for Face and Facial-Expression Recognition", Computing in Science & Engineering, vol. 13, no. , pp. 9-13, May/June 2011, doi:10.1109/MCSE.2010.149
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