Comparison of different PCA based Face Recognition algorithms using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  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
  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
  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