Improving Minutiae Detection in Fingerprints Using Multiresolution Contrast Enhancement

  • Angelo Chianese
  • Vincenzo Moscato
  • Antonio Penta
  • Antonio Picariello
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


The majority of automatic fingerprint matching systems depends on the comparison of the local ridge characteristics (bifurcation and termination), and a critical step in fingerprint matching is to extract minutiae from the input image. In this work we propose a novel ridge following algorithm based on a robust image enhancement filtering. Several experiments are carried out, showing the performances of the proposed approach.


Gray Level Local Contrast Fingerprint Image Ridge Line Minutia Extraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vision Graphics Image Process 29, 273–285 (1985)CrossRefGoogle Scholar
  2. 2.
    Abutaleb, A.S., Kamel, M.: A genetic algorithm for the estimation of ridges in fingerprints. Image Processing 8(8), 1134–1139 (1999)CrossRefGoogle Scholar
  3. 3.
    Arcelli, C., di Baja, G.S.: A width-independent fast thinning algorithm. IEEE Transanctions Pattern Analisys Machine Intelligence 7(4), 463–474 (1985)CrossRefGoogle Scholar
  4. 4.
    Bazen, A.M., Gerez, S.H.: Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 905–919 (2002)CrossRefGoogle Scholar
  5. 5.
    Le Negrate, A., Beghdadi, A.: Contrast enhancement technique based on local detection of edges. Computer Vision, Graphics and Image Processing 46, 162–174 (1989)CrossRefGoogle Scholar
  6. 6.
    Boccignone, G.: A multiscale contrast enhancement method. In: Proceedings of Intenational Conference on Image Processing, vol. 1, pp. 306–309 (1997)Google Scholar
  7. 7.
    Boccignone, G., Picariello, A.: Multiscale Contrast Enhancement of Medical Images. In: International Conference on Acoustics, Speech, and Signal Processing, vol. IV, pp. 2792–2798 (1997)Google Scholar
  8. 8.
    Bolle, R., Connell, J., Pankanti, S., Ratha, N., Senior, A.: Guide to Biometrics. Springer, Heidelberg (2003)Google Scholar
  9. 9.
    Domeniconi, C., Tari, S., Liang, P.: Direct Gray Scale Ridge Reconstruction in Fingerprint Images. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Seattle, Washington (1998)Google Scholar
  10. 10.
    Jain, A.K., Maltoni, D., Maio, D., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, Heidelberg (2003)MATHGoogle Scholar
  11. 11.
    Monro, D.M., Sherlock, D., Millard, K.: Fingerprint enhancement by directional fourier filtering. IEE Visual Image Signal Processing 141(2), 87–94 (1994)CrossRefGoogle Scholar
  12. 12.
    Greenberg, S., Aladjem, M., Kogan, D.: Fingerprint image enhancement using filtering techniques. Real-Time Imaging 8(3), 227–236 (2002)MATHCrossRefGoogle Scholar
  13. 13.
    Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: Algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)CrossRefGoogle Scholar
  14. 14.
    Jeng-Horng, C., Kuo-Chin, F.: Fingerprint ridge allocation in direct gray-scale domain. Pattern Recognition 34(10), 1907–1925 (2001)MATHCrossRefGoogle Scholar
  15. 15.
    Liu, J., Huang, Z., Chan, K.: Direct Minutiae Extraction from Gray-level Fingerprint Image by Relationship Examination. In: Proceedings of Internetionl Conference Image Processing, 2nd edn., pp. 427–430 (2000)Google Scholar
  16. 16.
    Maio, D., Maltoni, D.: Direct Gray-Scale Minutiae Detection in Fingerprints. IEEE Transanctions Pattern Analisys Machine Intelligence 19(1), 27–40 (1997)CrossRefGoogle Scholar
  17. 17.
    Jain, A.K., Ratha, N.K., Chen, S.: Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recognition 28(11), 1657–1672 (1995)CrossRefGoogle Scholar
  18. 18.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)CrossRefGoogle Scholar
  19. 19.
    Ratha, N.K., Bolle, R.M., Pankanti, S., Haas, N.: Quantifying quality: A case study in fingerprints. In: Proceedings of IEEE Conference on AutoID 2002 (March 2002)Google Scholar
  20. 20.
    Watson, C.I., Wilson, C.I.: Fingerprint DataBase, National Istitute of Standards of techonologyGoogle Scholar
  21. 21.
    Tian, J., Luo, X.P.: Knowledge based fingerprint image enhancement. In: Proceedings of Internetional Conference Pattern Recognition, Barcelona, Spain, vol. 3, pp. 783–786 (2000)Google Scholar
  22. 22.
    Jiang, X., Wei-Yun, Y., Ser, W.: Minutiae Extraction by Adaptive Tracing the Gray Level Ridge of the Fingerprint Image. In: Proceedings of Internetionl Conference Image Processing, vol. 2, pp. 852–856 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Angelo Chianese
    • 1
  • Vincenzo Moscato
    • 1
  • Antonio Penta
    • 1
  • Antonio Picariello
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaUniversity Federico IINaplesItaly

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