Face Recognition Using DCT and Hierarchical RBF Model

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

@InProceedings{Chen:2006:IDEAL,
  author =       "Yuehui Chen and Yaou Zhao",
  title =        "Face Recognition Using DCT and Hierarchical RBF
                 Model",
  booktitle =    "Intelligent Data Engineering and Automated Learning,
                 IDEAL 2006",
  year =         "2009",
  editor =       "Emilio Corchado and Hujun Yin and Vicente Botti and 
                 Colin Fyfe",
  volume =       "4224",
  series =       "Lecture Notes in Computer Science",
  pages =        "355--362",
  address =      "Burgos, Spain",
  month =        sep # " 20-23",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, DE, ECGP",
  isbn13 =       "978-3-540-45485-4",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.482.9685",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.482.9685",
  URL =          "http://cilab.ujn.edu.cn/paper/ideal1.pdf",
  DOI =          "doi:10.1007/11875581_43",
  abstract =     "This paper proposes a new face recognition approach by
                 using the Discrete Cosine Transform (DCT) and
                 Hierarchical Radial Basis Function Network (HRBF)
                 classification model. The DCT is employed to extract
                 the input features to build a face recognition system,
                 and the HRBF is used to identify the faces. Based on
                 the pre-defined instruction/operator sets, a HRBF model
                 can be created and evolved. This framework allows input
                 features selection. The HRBF structure is developed
                 using Extended Compact Genetic Programming (ECGP) and
                 the parameters are optimised by Differential Evolution
                 (DE). Empirical results indicate that the proposed
                 framework is efficient for face recognition.",
}

Genetic Programming entries for Yuehui Chen Yaou Zhao

Citations