• DocumentCode
    3459718
  • Title

    The Research on High Resolution Offline Palmprint Matching Algorithm

  • Author

    Shi, Guangshun ; Liang, Qian ; Tan, Zechao

  • Author_Institution
    Nankai Inst. of machine Intell., Tianjin, China
  • fYear
    2010
  • fDate
    21-23 Oct. 2010
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    The matching method of high resolution palmprint is one of the research focus in Biometrics Recognition area.lt includes key problems as palmprint alignment, similarity computing ,optimization of matching process and so on. This paper proposes a novel single-ring-four-sectors local structure and a new method of datum mark election, as well as a minutiae-based palmprint matching algorithm. The paper also presents an effective similarity computing approach and a multi-level palmprint matching approach to improve matching efficiency and accuracy. We use 44 latent palmprints and 10,400 archive palmprints which were from criminal investigation field to do experimental evaluation. As the experiments shows, the proportion for the correct results ranked first in the indentification of candidate reach to 78%, and the proportion for top 20 reach to 86%. The matching time for single palmprints is only 86ms.Compared with other existing, our approach has been a mark improvement both in identification accuracy and recognition speed.
  • Keywords
    biometrics (access control); fingerprint identification; image matching; image resolution; biometrics recognition; datum mark election; high resolution offline palmprint matching algorithm; similarity computing approach; Accuracy; Algorithm design and analysis; Biometrics; Electronic mail; Fingerprint recognition; Pattern matching;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition (CCPR), 2010 Chinese Conference on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-1-4244-7209-3
  • Electronic_ISBN
    978-1-4244-7210-9
  • Type

    conf

  • DOI
    10.1109/CCPR.2010.5659333
  • Filename
    5659333