Direct Pore Matching for Fingerprint Recognition

  • Qijun Zhao
  • Lei Zhang
  • David Zhang
  • Nan Luo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


Sweat pores on fingerprints have proven to be useful features for personal identification. Several methods have been proposed for pore matching. The state-of-the-art method first matches minutiae on the fingerprints and then matches the pores based on the minutia matching results. A problem of such minutia-based pore matching method is that the pore matching is dependent on the minutia matching. Such dependency limits the pore matching performance and impairs the effectiveness of the fusion of minutia and pore match scores. In this paper, we propose a novel direct approach for matching fingerprint pores. It first determines the correspondences between pores based on their local features. It then uses the RANSAC (RANdom SAmple Consensus) algorithm to refine the pore correspondences obtained in the first step. A similarity score is finally calculated based on the pore matching results. The proposed pore matching method successfully avoids the dependency of pore matching on minutia matching results. Experiments have shown that the fingerprint recognition accuracy can be greatly improved by using the method proposed in this paper.


Fingerprint recognition pore matching level-3 features fusion 


  1. 1.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, New York (2003) Google Scholar
  2. 2.
    Ratha, N., Bolle, R.: Automatic Fingerprint Recognition Systems. Springer, New York (2004) Google Scholar
  3. 3.
    CDEFFS: Data Format for the Interchange of Extended Fingerprint and Palmprint Features, Working Draft Version 0.2 (2008), Google Scholar
  4. 4.
    Ashbaugh, D.R.: Quantitative-Qualitative Friction Ridge Analysis: An Introduction to Basic and Advanced Ridgeology. CRC Press, Boca Raton (1999) Google Scholar
  5. 5.
    Stosz, J.D., Alyea, L.A.: Automated System for Fingerprint Authentication Using Pores and Ridge Structure. In: SPIE Conference on Automatic Systems for the Identification and Inspection of Humans, vol. 2277, pp. 210–223 (1994) Google Scholar
  6. 6.
    Kryszczuk, K., Drygajlo, A., Morier, P.: Extraction of Level 2 and Level 3 Features for Fragmentary Fingerprints. In: Second COST Action 275 Workshop, pp. 83–88 (2004) Google Scholar
  7. 7.
    Kryszczuk, K., Morier, P., Drygajlo, A.: Study of the Distinctiveness of Level 2 and Level 3 Features in Fragmentary Fingerprint Comparison. In: Maltoni, D., Jain, A.K. (eds.) BioAW 2004. LNCS, vol. 3087, pp. 124–133. Springer, Heidelberg (2004) Google Scholar
  8. 8.
    Jain, A.K., Chen, Y., Demirkus, M.: Pores and Ridges: Fingerprint Matching Using Level 3 Features. In: 18th International Conference on Pattern Recognition, vol. 4, pp. 477–480 (2006) Google Scholar
  9. 9.
    Jain, A.K., Chen, Y., Demirkus, M.: Pores and Ridges: Fingerprint Matching Using Level 3 Features. IEEE Trans. Pattern Analysis and Machine Intelligence 29(1), 15–27 (2007) Google Scholar
  10. 10.
    Roddy, A., Stosz, J.: Fingerprint Features - Statistical Analysis and System Performance Estimates. Proceedings of the IEEE 85(9), 1390–1421 (1997) Google Scholar
  11. 11.
    Maio, D., Maltoni, D.: Direct Gray-Scale Minutiae Detection in Fingerprints. IEEE Trans. Pattern Analysis and Machine Intelligence 19(1), 27–40 (1997) Google Scholar
  12. 12.
    Jiang, X., Yau, W.Y., Ser, W.: Minutiae Extraction by Adaptive Tracing the Gray Level Ridge of the Fingerprint Image. In: ICIP 1999, vol. 2, pp. 852–856 (1999) Google Scholar
  13. 13.
    Chen, X., Tian, J., Cheng, J., Yang, X.: Segmentation of Fingerprint Images Using Linear Classifier. EURASIP Journal on Applied Signal Processing 2004(4), 480–494 (2004) Google Scholar
  14. 14.
    Wu, C., Tulyakov, S., Govindaraju, V.: Robust Point-Based Feature Fingerprint Segmentation Algorithm. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 1095–1103. Springer, Heidelberg (2007) Google Scholar
  15. 15.
    Zhao, Q., Zhang, L., Zhang, D., Luo, N., Bao, J.: Adaptive Pore Model for Fingerprint Pore Extraction. In: ICPR 2008 (2008) Google Scholar
  16. 16.
    Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. IEEE Trans. Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005) Google Scholar
  17. 17.
    Jain, A.K., Prabhakar, S., Hong, L., Pankanti, S.: Filterbank-based Fingerprint Matching. IEEE Trans. Image Processing 9(5), 846–859 (2000) Google Scholar
  18. 18.
    Fishler, M., Bolles, R.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Comm. ACM 24(6), 381–395 (1981) Google Scholar
  19. 19.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge Univ., Cambridge (2003) Google Scholar
  20. 20.
    Phillips, J.M., Liu, R., Tomasi, C.: Outlier Robust ICP for Minimizing Fractional RMSD. In: 6th International Conference on 3-D Digital Imaging and Modeling, pp. 427–434 (2007) Google Scholar
  21. 21.
    Feng, J.: Combining Minutiae Descriptors for Fingerprint Matching. Pattern Recognition 41, 342–352 (2008) Google Scholar
  22. 22.
    Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC 2002: Second Fingerprint Verification Competition. In: ICPR 2002, pp. 811–814 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Qijun Zhao
    • 1
  • Lei Zhang
    • 1
  • David Zhang
    • 1
  • Nan Luo
    • 1
  1. 1.Biometrics Research Centre, Department of ComputingThe Hong Kong Polytechnic UniversityKowloonHong Kong

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