• DocumentCode
    2347755
  • Title

    SVM-based Discriminant Analysis for face recognition

  • Author

    Kim, Sang-Ki ; Toh, Kar-Ann ; Lee, Sangyoun

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Yonsei Univ., Seoul
  • fYear
    2008
  • fDate
    3-5 June 2008
  • Firstpage
    2112
  • Lastpage
    2115
  • Abstract
    In this paper, we introduce a novel variant of Linear Discriminant Analysis (LDA) for face recognition. The proposed method attempts to find an optimal LDA matrix by redesigning the between-class scatter matrix incorporating a Support Vector Machine (SVM). Our empirical evaluations show that the proposed method offers noticeable performance improvement over the conventional LDA.
  • Keywords
    face recognition; matrix algebra; support vector machines; SVM-based discriminant analysis; face recognition; linear discriminant analysis; optimal LDA matrix; support vector machine; Biometrics; Eigenvalues and eigenfunctions; Face recognition; Feature extraction; Independent component analysis; Kernel; Linear discriminant analysis; Principal component analysis; Scattering; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics and Applications, 2008. ICIEA 2008. 3rd IEEE Conference on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4244-1717-9
  • Electronic_ISBN
    978-1-4244-1718-6
  • Type

    conf

  • DOI
    10.1109/ICIEA.2008.4582892
  • Filename
    4582892
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2347755