Advertisement

A Near-linear Time Algorithm for Binarization of Fingerprint Images Using Distance Transform

  • Xuefeng Liang
  • Arijit Bishnu
  • Tetsuo Asano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3322)

Abstract

Automatic Fingerprint Identification Systems (AFIS) have various applications to biometric authentication, forensic decision, and many other areas. Fingerprints are useful for biometric purposes because of their well known properties of distinctiveness and persistence over time. Fingerprint images are characterized by alternating spatial distribution of gray-level intensity values of ridges and ravines/valleys of almost equal width. Most of the fingerprint matching techniques require extraction of minutiae that are the terminations and bifurcations of the ridge lines in a fingerprint image. Crucial to this step, is either detecting ridges from the gray-level image or binarizing the image and then extracting the minutiae. In this work, we focus on binarization of fingerprint images using linear time euclidean distance transform algorithms. We exploit the property of almost equal widths of ridges and valleys for binarization. Computing the width of arbitrary shapes is a non-trivial task. So, we estimate width using distance transform and provide an O(N 2 log M) time algorithm for binarization where M is the number of gray-level intensity values in the image and the image dimension is N × N. With M for all purposes being a constant, the algorithm runs in near-linear time in the number of pixels in the image.

Keywords

Binary Image Voronoi Diagram Dynamic Time Warping Fingerprint Image Equal Width 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Automated Classification System Reader Project (ACS), Technical Report, DeLaRue Printrak Inc. (February 1985)Google Scholar
  2. 2.
    Bhanu, B., Tan, X.: Fingerprint Indexing Based on Novel Features of Minutiae Triplets. IEEE Trans. PAMI 25(5), 616–622 (2003)Google Scholar
  3. 3.
    Blue, J.L., Candela, G.T., Grother, P.J., Chellappa, R., Wilson, C.L., Blue, J.D.: Evaluation of Pattern Classifiers for Fingerprint and OCR Application. Pattern Recognition 27(4), 485–501 (1994)CrossRefGoogle Scholar
  4. 4.
    Breu, H., Gil, J., Kirkpatrick, D., Werman, M.: Linear Time Euclidean Distance Transform Algorithms. IEEE Trans. PAMI 17(5), 529–533 (1995)Google Scholar
  5. 5.
    Candela, G.T., Grother, P.J., Watson, C.I., Wilkinson, R.A., Wilson, C.L.: PCASYS - A Pattern-Level Classification Automation System for Fingerprints. NISTIR 5647, August 1995, National Institute of Standards and Technology (1995)Google Scholar
  6. 6.
    Chang, J.-H., Fan, K.-C.: Fingerprint Ridge Allocation in Direct Gray-Scale Domain. Pattern Recognition 34(10), 1907–1925 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Coetzee, L., Botha, E.C.: Fingerprint Recognition in Low Quality Images. Pattern Recognition 26(10), 1441–1460 (1993)CrossRefGoogle Scholar
  8. 8.
    Douglas Hung, D.C.: Enhancement and Feature Purification of Fingerprint Images. Pattern Recognition 26(11), 1661–1771 (1993)CrossRefGoogle Scholar
  9. 9.
    Farina, A., Kovács-Vajna, Z.M., Leone, A.: Fingerprint Minutiae Extraction from Skeletonized Binary Images. Pattern Recognition 32, 877–889 (1999)CrossRefGoogle Scholar
  10. 10.
    Fingerprint Verification Competition (2000), http://bias.csr.unibo.it/fvc2000/download.asp
  11. 11.
    Galton, F.: Fingerprints. Macmillan, London (1892)Google Scholar
  12. 12.
    Haralick, R.: Ridges and Valleys on Digital Images. Computer Vision Graphics Image Processing 22, 28–38 (1983)CrossRefGoogle Scholar
  13. 13.
    Hirata, T., Katoh, T.: An Algorithm for Euclidean distance transformation. SIGAL Technical Report of IPS of Japan, 94-AL-41-4, 25–31 (September 1994)Google Scholar
  14. 14.
    Hollingum, J.: Automated Fingerprint Analysis Offers Fast Verification. Sensor Review 12(13), 12–15 (1992)CrossRefGoogle Scholar
  15. 15.
    Hong, L., Wan, Y., Jain, A.K.: Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Trans. PAMI 20(8), 777–789 (1998)Google Scholar
  16. 16.
    Jain, A.K., Hong, L., Bolle, R.: On-Line Fingerprint Verification. IEEE Trans. PAMI 19(4), 302–314 (1997)Google Scholar
  17. 17.
    Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An Identity-Authentication System Using Fingerprints. Proc. of IEEE 85(9), 1365–1388 (1997)CrossRefGoogle Scholar
  18. 18.
    Kovács-Vajna, Z.M.: A Fingerprint Verification System Based on Triangular Matching and Dynamic Time Warping. IEEE Trans. PAMI 22(11), 1266–1276 (2000)Google Scholar
  19. 19.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, New York (2003)zbMATHGoogle Scholar
  20. 20.
    Maio, D., Maltoni, D.: Direct Gray-Scale Minutiae Detection In Fingerprints. IEEE Trans. PAMI 19(1), 27–39 (1997)Google Scholar
  21. 21.
    Mehtre, B.M., Chatterjee, B.: Segmentation of Fingerprint Images - A Composite Method. Pattern Recognition 22, 381–385 (1989)CrossRefGoogle Scholar
  22. 22.
    Mehtre, B.M.: Fingerprint Image Analysis for Automatic Identification. Machine Vision and Applications 6(2), 124–139 (1993)CrossRefGoogle Scholar
  23. 23.
    Moayer, B., Fu, K.: A Tree System Approach for Fingerprint Pattern Recognition. IEEE Trans. PAMI 8(3), 376–388 (1986)Google Scholar
  24. 24.
    O’Gorman, L., Nickerson, J.V.: An Approach to Fingerprint Filter Design. Pattern Recognition 22, 29–38 (1989)CrossRefGoogle Scholar
  25. 25.
    Ratha, N.K., Chen, S.Y., Jain, A.K.: Adaptive Flow Orientation-Based Feature Extraction in Fingerprint Images. Pattern Recognition 28(11), 1657–1672 (1995)CrossRefGoogle Scholar
  26. 26.
    Rosenfeld, A., Kak, A.C.: Digital Image Processing, vol. 2. Academic Press Inc., Orlando (1982)Google Scholar
  27. 27.
    Senior, A.: A Combination Fingerprint Classifier. IEEE Trans. PAMI 23(10), 1165–1174 (2001)Google Scholar
  28. 28.
    Watson, C.I., Wilson, C.L.: Fingerprint Database, National Institute of Standards and Technology, Special Database 4, FPDB (April 1992)Google Scholar
  29. 29.
    Wegstein, J.H.: An Automated Fingerprint Identification System. US Government Publication, Washington (1982)Google Scholar
  30. 30.
    Xiao, Q., Raafat, H.: Fingerprint Image Post-Processing: A Combined Statistical and Structural Approach. Pattern Recognition 24(10), 985–992 (1991)CrossRefGoogle Scholar
  31. 31.
    Shih, F.Y., Pu, C.C.: A Skeletonization Algorithm by Maxima Tracking on Euclidean Distance Transform. Pattern Recognition 28(3), 331–341 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Xuefeng Liang
    • 1
  • Arijit Bishnu
    • 1
  • Tetsuo Asano
    • 1
  1. 1.JAISTTatsunokuchiJapan

Personalised recommendations