• DocumentCode
    2655962
  • Title

    Facial expression recognition using RBF neural network based on improved artificial fish swarm algorithm

  • Author

    Ye, Wang ; Xiaojun, Wu ; Shitong, Wang ; Jingyu, Yang

  • Author_Institution
    Sch. of Inf. Technol., Jiangnan Univ., Wuxi
  • fYear
    2008
  • fDate
    16-18 July 2008
  • Firstpage
    416
  • Lastpage
    420
  • Abstract
    Artificial fish swarm algorithm (AFSA) is a global optimization method proposed recently. After analyzing the disadvantages of AFSA, this paper introduced best-step operator and refined the prey behavior. An improved artificial fish-swarm algorithm for the RBF neural network and a model based on this method is developed. Finally the new algorithm is applied to the problem of expression recognition. The research indicates that the new algorithm has some advantages in terms of convergence performance, recognition rate and so on.
  • Keywords
    face recognition; optimisation; radial basis function networks; RBF neural network; artificial fish swarm algorithm; facial expression recognition; global optimization method; Active shape model; Artificial neural networks; Eyebrows; Eyes; Face recognition; Feature extraction; Humans; Marine animals; Morphology; Mouth; Artificial fish-swarm algorithm; Best-step; Facial expression recognition; RBF NN;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference, 2008. CCC 2008. 27th Chinese
  • Conference_Location
    Kunming
  • Print_ISBN
    978-7-900719-70-6
  • Electronic_ISBN
    978-7-900719-70-6
  • Type

    conf

  • DOI
    10.1109/CHICC.2008.4604925
  • Filename
    4604925