• DocumentCode
    2497761
  • Title

    Research on synergetic fingerprint classification and matching

  • Author

    Gao, Jun ; Dong, Huo-Ming ; Chen, Dingguo ; Gan, Long ; Dong, Wen-Wen

  • Author_Institution
    Sch. of Comput. & Inf., Hefei Univ. of Technol., China
  • Volume
    5
  • fYear
    2003
  • fDate
    2-5 Nov. 2003
  • Firstpage
    3066
  • Abstract
    Fingerprint recognition is one of the research hotspots of biometrics techniques. And fingerprint classification and matching are key parts in an automated fingerprint recognition system. The traditional fingerprint recognition systems have such disadvantages as high computation complexity, low speed, low recognition rate to uncompleted or defiled fingerprints, and not robust. In this paper, we propose a novel fingerprint classification and matching method based on synergetic pattern recognition, which emphasizes global features of fingerprint. With lots of artificial fingerprint samples, the results show that the proposed method is effective, fast and robust. In the end, experimental results are analyzed and a synergetic fingerprint recognition system is introduced.
  • Keywords
    fingerprint identification; image classification; image matching; artificial fingerprint samples; automated fingerprint recognition system; biometrics techniques; computation complexity; defiled fingerprints; fingerprint matching; recognition rate; synergetic fingerprint classification; synergetic pattern recognition; Biometrics; Data mining; Fingerprint recognition; Gallium nitride; Image matching; Information security; Neural networks; Pattern formation; Pattern recognition; Robustness;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2003 International Conference on
  • Print_ISBN
    0-7803-8131-9
  • Type

    conf

  • DOI
    10.1109/ICMLC.2003.1260104
  • Filename
    1260104