Fingerprint Verification Using Local Interest Points and Descriptors

  • Javier Ruiz-del-Solar
  • Patricio Loncomilla
  • Christ Devia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)


A new approach to automatic fingerprint verification based on a general-purpose wide baseline matching methodology is here proposed. The approach is not based on the standard ridge-minutiae-based framework. Instead of detecting and matching the standard structural features, local interest points are detected in the fingerprints, then local descriptors are computed in the neighborhood of these points, and afterwards these descriptors are compared using local and global matching procedures. Then, a final verification is carried out by a Bayes classifier. The methodology is validated using the FVC2004 dataset, where competitive results are obtained.


Fingerprint verification Wide Baseline Matching SIFT 


  1. 1.
    Bazen, A.M., Gerez, S.H.: Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Trans. Pattern Anal. Machine Intell. 24(7), 905–919 (2002)CrossRefGoogle Scholar
  2. 2.
    Cappelli, R., Maio, D., Maltoni, D., Wayman, J., Jain, A.K.: Performance evaluation of fingerprint verification systems. IEEE Trans. Pattern Anal. Machine Intell. 28(1), 3–18 (2006)CrossRefGoogle Scholar
  3. 3.
    Ferrari, V., Tuytelaars, T., Van Gool, L.: Simultaneous Object Recognition and Segmentation by Image Exploration. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 40–54. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. 4th Alvey Vision Conf., pp. 147–151. Manchester, UK (1998)Google Scholar
  5. 5.
    Lee, H.C., Gaensslen, R.E.: Advances in Fingerpint Tecnology. Elsevier, NY (1991)Google Scholar
  6. 6.
    Lowe, D.: Local feature view clustering for 3D object recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, pp. 682–688. IEE Press, New York (2001)Google Scholar
  7. 7.
    Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. Int. Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Loncomilla, P., Ruiz-del-Solar, J.: Gaze Direction Determination of Opponents and Teammates in Robot Soccer. In: Cabonell, J.G., Siekmann, J. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 230–242. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Loncomilla, P., Ruiz-del-Solar, J.: A Fast Probabilistic Model for Hypothesis Rejection in SIFT-Based Object Recognition. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol. 4225, pp. 696–705. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, NY (2003)zbMATHGoogle Scholar
  11. 11.
    Mikolajczyk, K., Schmid, C.: Scale & Affine Invariant Interest Point Detectors. Int. Journal of Computer Vision 60(1), 63–96 (2004)CrossRefGoogle Scholar
  12. 12.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Machine Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  13. 13.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A Comparison of Affine Region Detectors. Int. Journal of Computer Vision 65(1-2), 43–72 (2005)CrossRefGoogle Scholar
  14. 14.
    Ross, A., Dass, S., Jain, A.K.: Fingerprint warping using ridge curve correspondences. IEEE Trans. Pattern Anal. Machine Intell. 28(1), 19–30 (2006)CrossRefGoogle Scholar
  15. 15.
    Ruiz-del-Solar, J., Devia, C., Loncomilla, P., Concha, F.: Offline Signature Verification using Local Interest Points and Descriptors. In: Proc. CIARP 2008. LNCS, vol. 5197. Springer, Heidelberg (2008)Google Scholar
  16. 16.
    Schaffalitzky, F., Zisserman, A.: Automated location matching in movies. Computer Vision and Image Understanding 92(2-3), 236–264 (2003)CrossRefzbMATHGoogle Scholar
  17. 17.
    Kovacs-Vajna, Z.M.: A fingerprint verification system based on triangular matching and dynamic time warping. IEEE Trans. Pattern Anal. Machine Intell. 22(11), 1266–1276 (2000)CrossRefGoogle Scholar
  18. 18.
  19. 19.
  20. 20.
    Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: Algorithm and performance evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 777–789 (1998)CrossRefGoogle Scholar
  21. 21.
    Wang, Y., Hu, J., Han, F.: Enhanced gradient-based algorithm for the estimation of fingerprint orientation fields. Applied Mathematics and Computation 185, 823–833 (2007)CrossRefzbMATHGoogle Scholar
  22. 22.
    Prabhakar, S., Jain, A.K., Pankanti, S.: Learning fingerprint minutiae location and type. Pattern Recognition 36, 1847–1857 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Javier Ruiz-del-Solar
    • 1
  • Patricio Loncomilla
    • 1
  • Christ Devia
    • 1
  1. 1.Department of Electrical EngineeringUniversidad de ChileChile

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