Issue No. 03 - May/June (2011 vol. 13)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MCSE.2010.149
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.
face recognition, feature extraction, genetic algorithms, principal component analysis
"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