Comparison of different PCA based Face Recognition algorithms using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Bozorgtabar:2010:IST,

• author = "Behzad Bozorgtabar and Farzad Noorian and Gholam Ali Rezai Rad",
• title = "Comparison of different PCA based Face Recognition algorithms using Genetic Programming",
• booktitle = "5th International Symposium on Telecommunications (IST 2010)",
• year = "2010",
• month = dec,
• pages = "801--805",
• abstract = "Face Recognition plays a vital role in automation of security systems; therefore many algorithms have been invented with varying degrees of effectiveness. After successful try out of principal component analyses (PCA) in eigenfaces method, many different PCA based algorithms such as Two Dimensional PCA (2DPCA) and Multilinear PCA (MLPCA), combined with several classifying algorithms were studied. This paper uses Genetic Programming (GP) as a clustering tool, to classify features extracted by PCA, 2DPCA and MLPCA. Results of different algorithms are compared with each other and also previous studies and it is shown that Genetic Programming can be used in combination with PCA for face recognition problems.",
• keywords = "genetic algorithms, genetic programming, eigenfaces method, face recognition algorithms, multilinear PCA, principal component analyses, security systems automation, two dimensional PCA, eigenvalues and eigenfunctions, face recognition, principal component analysis",
• DOI = "doi:10.1109/ISTEL.2010.5734132",
• notes = "Also known as \cite{5734132}",

}

Genetic Programming entries for Behzad Bozorgtabar Farzad Noorian Rezai Rad Gholam-Ali

Citations