Fingerprint Recognition Based on Combined Features

  • Yangyang Zhang
  • Xin Yang
  • Qi Su
  • Jie Tian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


In this paper, we represent the fingerprint with a novel local feature descriptor, which is composed of minutia, the sample points on associated ridge and the adjacent orientation distribution. Then a novel fingerprint recognition method is proposed combining the orientation field and the local feature descriptor. We compare two descriptor lists from the input and template fingerprints to calculate a set of transformation vectors for fingerprint alignment. The similarity score is evaluated by fusing the orientation field and the local feature descriptor. The experiments have been conducted on three large-scale databases. The comparison results approve that our algorithm is more accurate and robust than previous methods based on the minutiae or ridge features, especially for those poor-quality and partial fingerprints.


orientation field local feature descriptor fingerprint alignment fusing similarity score 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yangyang Zhang
    • 1
  • Xin Yang
    • 1
  • Qi Su
    • 1
  • Jie Tian
    • 1
  1. 1.Center for Biometrics and Security Research, Key Laboratory of Complex Systems, and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences, P.O.Box 2728 Beijing 100080, Email:tian@ieee.orgChina

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