• DocumentCode
    1691581
  • Title

    GPU based Partially Connected Neural Evolutionary network and its application on gender recognition with face images

  • Author

    Chen, Xiao-Xi ; Shi, Ming-Hui ; De Garis, Hugo

  • Author_Institution
    Dept. of Comput. Sci., Xiamen Univ., Xiamen, China
  • fYear
    2010
  • Firstpage
    1930
  • Lastpage
    1934
  • Abstract
    An algorithm for evolving neural network via the genetic algorithm based on GPU parallel architecture was implemented on the CUDA, resulting in a system called CuParcone (CUDA based Partially Connected Neural Evolutionary) and was used on gender face recognition. By using the powerful ability of GPU parallel computing, CuParcone achieves a performance increase about 323 times than Parcone algorithm, which runs on a single-processor. With this new model, a gender recognition experiment was made on 530 face images (265 females and 265 males from Color FERET database), including not only frontal faces but also the faces rotated from -40°~40° in the direction of horizontal, and achieved the accuracy rate of 90.84%.
  • Keywords
    computer graphics; face recognition; neural nets; parallel architectures; CuParcone; GPU; Parcone algorithm; face images; face recognition; gender recognition; parallel architecture; partially connected neural evolutionary network; Computational modeling; Face; Face recognition; Graphics processing unit; Image color analysis; Image recognition; Support vector machines; CUDA; gender recognition; neural networks; parallel computing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation (WCICA), 2010 8th World Congress on
  • Conference_Location
    Jinan
  • Print_ISBN
    978-1-4244-6712-9
  • Type

    conf

  • DOI
    10.1109/WCICA.2010.5554600
  • Filename
    5554600