• DocumentCode
    3309051
  • Title

    Application of Boolean Kernel Function SVM in Face Recognition

  • Author

    Cui, Kebin ; Du, Yingshuag

  • Author_Institution
    Sch. of Comput. Sci. & Technol., North China Electr. Power Univ., Baoding
  • Volume
    1
  • fYear
    2009
  • fDate
    25-26 April 2009
  • Firstpage
    619
  • Lastpage
    622
  • Abstract
    SVM based on Boolean kernel function has outstanding performance in classifying, for the problem of face recognition, recognizing strategies based on MDNF and MPDNF Boolean kernel function SVM are Proposed. Firstly, Karhunen-Loeve transform is employed to get the representation basis of face image set, secondly, the extracted characteristics is translated into 0-1 format, thirdly, SVM based Boolean kernel function are used to classify. The face recognition experiments with ORL face databases show that the proposed methods led to significantly better recognition accuracy compared with traditional PCA method and linear SVM, between the proposed methods, the one based on MPDNF Boolean kernel function get better performance.
  • Keywords
    Boolean functions; Karhunen-Loeve transforms; face recognition; image classification; image representation; polynomials; support vector machines; Karhunen-Loeve transform; MPDNF Boolean kernel function; face recognition; image classification; image representation; monotone polynomial disjunctive normal form; support vector machine; Artificial intelligence; Computer science; Eigenvalues and eigenfunctions; Face recognition; Karhunen-Loeve transforms; Kernel; Pattern recognition; Principal component analysis; Support vector machine classification; Support vector machines; Boolean kernel function; Karhunen-Loeve transform; face recognition; multi-classification; support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Networks Security, Wireless Communications and Trusted Computing, 2009. NSWCTC '09. International Conference on
  • Conference_Location
    Wuhan, Hubei
  • Print_ISBN
    978-1-4244-4223-2
  • Type

    conf

  • DOI
    10.1109/NSWCTC.2009.172
  • Filename
    4908341