• DocumentCode
    3014034
  • Title

    A Face Recognition System Using Support Vector Machines and Elastic Graph Matching

  • Author

    Li, Yun-Feng

  • Author_Institution
    Electromech. Eng. Collage, Henan Univ. of Sci. & Technol., Luoyang, China
  • Volume
    1
  • fYear
    2009
  • fDate
    7-8 Nov. 2009
  • Firstpage
    3
  • Lastpage
    6
  • Abstract
    An efficient face recognition system by the combination of support vector machines (SVMs) and elastic graph matching was presented. The implementations of this system are as follows: Firstly, the two eyes of a face image are detected by using SVMs approach, and the detected eye coordinates are used as reference points for alignment and normalization of the face image. Secondly, elastic graph matching is performed to locate the facial feature points. The preprocessed face images have the same size and the eyes lie at the uniform positions, elastic graph matching is limited to local distortions only, so the matching time can be reduced considerably. Lastly, local facial features are extracted for face register or recognition, recognition is performed by comparing the similarity of the local facial features of a test image to all trained images. The experimental results demonstrate the effectiveness of this face recognition system.
  • Keywords
    face recognition; feature extraction; graph theory; image matching; image registration; object detection; support vector machines; elastic graph matching; eye coordinate detection; face image normalization; face recognition system; face register; local facial feature point extraction; support vector machines; Artificial intelligence; Computational intelligence; Eyes; Face detection; Face recognition; Facial features; Image recognition; Performance evaluation; Support vector machines; Testing; Support Vector Machines; elastic graph matching; eye detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-3835-8
  • Electronic_ISBN
    978-0-7695-3816-7
  • Type

    conf

  • DOI
    10.1109/AICI.2009.149
  • Filename
    5375972