Fingerprint Matching Based on Neighboring Information and Penalized Logistic Regression

  • Kai Cao
  • Xin Yang
  • Jie Tian
  • Yangyang Zhang
  • Peng Li
  • Xunqiang Tao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


This paper proposes a novel minutiae-based fingerprint matching algorithm. A fingerprint is represented by minutiae set and sampling points on all ridges. Therefore, the foreground of a fingerprint image can be accurately estimated by the sampling points. The similarity between two minutiae is measured by two parts: neighboring minutiae which are different in minutiae pattern and neighboring sampling points which are different in orientation and frequency. After alignment and minutiae pairing, Nine features are extracted to represent the matching status and penalized logistic regression (PLR) is adopted to calculate the matching score. The proposed algorithm is evaluated on fingerprint databases of FVC2002 and compared with the participants in FVC 2002. Experimental results show that the proposed algorithm achieves good performance and ranks 5th according to average equal error rate.


Minutia fingerprint matching penalized logistic regression ridge 


  1. 1.
    Tico, M., Kuosmanen, P.: Fingerprint Matching Using an Orientation-Based Minutia Descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 1009–1014 (2003) Google Scholar
  2. 2.
    He, Y., Tian, J., Luo, X., Zhang, T.: Image Enhancement and Minutiae Matching in Fingerprint Verification. Pattern Recognition Letters 24(9), 1349–1360 (2003) Google Scholar
  3. 3.
    He, Y., Tian, J., Li, L., Chen, H., Yang, X.: Fingerprint Matching Based on Global Comprehensive Similarity. IEEE Trans. Pattern Anal. Mach. Intell. 28(6), 850–862 (2006) Google Scholar
  4. 4.
    Bazen, A.M., Gerez, S.H.: Fingerprint Matching by Thin-Plate Spline Modelling of Elastic Deformations. Pattern Recognition 36(8), 1859–1867 (2003) Google Scholar
  5. 5.
    Feng, J.: Combining Minutiae Descriptors for Fingerprint Matching. Pattern Recognition 41(1), 342–352 (2008) Google Scholar
  6. 6.
    Lin, H., Wan, Y., Jain, A.K.: Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998) Google Scholar
  7. 7.
    Luo, X., Tian, J.: Knowledge Based Fingerprint Image Enhancement. In: Proc. 15th ICPR, pp. 4783–4786 (2000) Google Scholar
  8. 8.
    Graham, R.L.: An Efficient Algorithm for Determining The Convex Hull of a Finite Planar Set. Information Processing Letters 26, 132–133 (1972) Google Scholar
  9. 9.
    Shen, L., Tan, E.T.: Dimension Reduction-Based Penalized Logistic Regression for Cancer Classification Using Microarray Data. IEEE/ACM Trans. On Computational Biology and Bioinformatics 2(2), 166–175 (2005) Google Scholar
  10. 10.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2001) Google Scholar
  11. 11.
    The 2nd fingerprint verification competition, Google Scholar
  12. 12.
    Wilson, C.L., Watson, C.I., Paek, E.G.: Effect of Resolution and Image Quality on Combined Optical and Neural Network Fingerprint Matching. Pattern Recognition 33(2), 317–331 (2000) Google Scholar
  13. 13.
    Feng, J., Ouyang, Z., Cai, A.: Fingerprint Matching Using Ridges. Pattern Recognition 39(11), 2131–2140 (2006) Google Scholar
  14. 14.
    Gu, J., Zhou, J., Yang, C.: Fingerprint Recognition by Combining Global Structure and Local Cues. IEEE Trans. On Image Processing 15(7), 1952–1964 (2006) Google Scholar
  15. 15.
    Jain, A.K., Hong, L., Bolle, R.: On-Line Fingerprint Verification. IEEE Trans.Pattern Anal. Mach. Intell. 19(4), 302–314 (1997) Google Scholar
  16. 16.
    Borgefors, G.: Distance Transformations in Digital Images. Computer Vision, Graphics, and Image Processing 34(3), 344–371 (1986)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kai Cao
    • 1
  • Xin Yang
    • 1
  • Jie Tian
    • 1
  • Yangyang Zhang
    • 1
  • Peng Li
    • 1
  • Xunqiang Tao
    • 1
  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina

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