• DocumentCode
    2709643
  • Title

    Gender Classification with Support Vector Machines Based on Non-tensor Pre-wavelets

  • Author

    Ying, Li ; Yu, Zhang ; Shishun, Zhao

  • Author_Institution
    Symbol Comput. & Knowledge Eng. Lab. of Minist. of Educ., Jilin Univ., Changchun, China
  • fYear
    2010
  • fDate
    7-10 May 2010
  • Firstpage
    770
  • Lastpage
    774
  • Abstract
    In this paper, a novel method for gender classifications with support vector machines based on our constructed bivariate compactly supported non-tensor product pre-wavelets is proposed. Utilizing the non-tensor product pre-wavelets to extract the more excellent gender classification features, then these features are fed into support vector machines to automatically perform gender classification. The combination of the non-tensor product pre-wavelets and SVMs for gender classification is demonstrated to be efficient by concrete numerical experiments.
  • Keywords
    face recognition; support vector machines; wavelet transforms; gender classification; nontensor prewavelets; support vector machines; Face recognition; Humans; Image processing; Mathematics; Nonlinear filters; Support vector machine classification; Support vector machines; Surveillance; Tensile stress; Wavelet transforms; gender classifications; pre-wavelets; support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Research and Development, 2010 Second International Conference on
  • Conference_Location
    Kuala Lumpur
  • Print_ISBN
    978-0-7695-4043-6
  • Type

    conf

  • DOI
    10.1109/ICCRD.2010.170
  • Filename
    5489499