Evolutionary feature synthesis for facial expression recognition

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

  author =       "Jiangang Yu and Bir Bhanu",
  title =        "Evolutionary feature synthesis for facial expression
  journal =      "Pattern Recognition Letters",
  year =         "2006",
  volume =       "27",
  number =       "11",
  pages =        "1289--1298",
  month =        aug,
  note =         "Evolutionary Computer Vision and Image Understanding",
  keywords =     "genetic algorithms, genetic programming, Feature
                 learning, Gabor filters",
  DOI =          "doi:10.1016/j.patrec.2005.07.026",
  abstract =     "Feature extraction is one of the key steps in object
                 recognition. In this paper we propose a novel
                 genetically inspired learning method for facial
                 expression recognition (FER). Unlike current research
                 on facial expression recognition that generally selects
                 visually meaningful feature by hands, our learning
                 method can discover the features automatically in a
                 genetic programming-based approach that uses Gabor
                 wavelet representation for primitive features and
                 linear/nonlinear operators to synthesise new features.
                 These new features are used to train a support vector
                 machine classifier that is used for recognising the
                 facial expressions. The learned operator and classifier
                 are used on unseen testing images. To make use of
                 random nature of a genetic program, we design a
                 multi-agent scheme to boost the performance. We compare
                 the performance of our approach with several approaches
                 in the literature and show that our approach can
                 perform the task of facial expression recognition

Genetic Programming entries for Jiangang Yu Bir Bhanu