Automatic Eye Detection in Face Images for Unconstrained Biometrics Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/semcco/PadoleA13,
  author =       "Chandrashekhar Padole and Joanne Athaide",
  title =        "Automatic Eye Detection in Face Images for
                 Unconstrained Biometrics Using Genetic Programming",
  booktitle =    "Proceedings of the 4th International Conference on
                 Swarm, Evolutionary, and Memetic Computing (SEMCCO
                 2013), Part II",
  year =         "2013",
  editor =       "Bijaya Ketan Panigrahi and 
                 Ponnuthurai Nagaratnam Suganthan and Swagatam Das and Subhransu Sekhar Dash",
  volume =       "8298",
  series =       "Lecture Notes in Computer Science",
  pages =        "364--375",
  address =      "Chennai, India",
  month =        dec # " 19-21",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-03755-4",
  bibdate =      "2013-12-18",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/semcco/semcco2013-2.html#PadoleA13",
  URL =          "http://dx.doi.org/10.1007/978-3-319-03756-1",
  URL =          "http://dx.doi.org/10.1007/978-3-319-03756-1_33",
  DOI =          "doi:10.1007/978-3-319-03756-1_33",
  abstract =     "Automatic extraction of eyes is a very important step
                 in face detection and recognition system since eyes are
                 one of the most stable features of the human face. In
                 this paper, we present a novel technique using genetic
                 programming for determining the classifier function to
                 be used in the automatic detection of eyes in facial
                 images. The feature terminals fed to the system are
                 Gabor wavelet filtered image, mean, standard deviation
                 and vertical position. Gabor wavelet transform has the
                 optimal basis to extract local features. To find the
                 Gabor wavelet to filter the image, we make use of
                 Levenberg-Marquardt optimisation. For the fitness
                 function, we have used the concept of localisation
                 fitness, which is incorporated in the calculation of
                 the precision and recall values to be included in
                 fitness. We tested our system on the face images from
                 the ORL databases and have presented our results. The
                 result shows the effectiveness and flexibility provided
                 by genetic programming in deciding the classifier for
                 the detection of eyes in face images.",
}

Genetic Programming entries for Chandrashekhar Padole Joanne Athaide

Citations