Fingerprint Ridge Distance Estimation: Algorithms and the Performance

  • Xiaosi Zhan
  • Zhaocai Sun
  • Yilong Yin
  • Yayun Chu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


Ridge distance is one important attribute of the fingerprint image and it also is one important parameter in the fingerprint enhancement. It is important for improving the AFIS’s performance to estimate the ridge distance correctly. The paper discusses the representative fingerprint ridge distance estimation algorithms and the performance of these algorithms. The most common fingerprint ridge distance estimation algorithm is based on block-level and estimates the ridge distance by calculating the number of cycle pattern in the block fingerprint image. The traditional Fourier transform spectral analysis method has been also applied to estimate the fingerprint ridge distance. The next kind of method is based on the statistical window. Another novel fingerprint ridge distance estimation method is based on the region-level which regards the region with the consistent orientation as the statistical region. One new method obtains the fingerprint ridge distance from the continuous Fourier spectrum. After discussing the dominant algorithm thought, the paper analyzes the performance of each algorithm.


  1. 1.
    Hong, L., Jain, A.K., Bolle, R., et al.: Identity authentication using fingerprints. In: Proceedings of FirstInternational Conference on Audio and Video-Based Biometric Person Authenti-cation, Switzerland, pp. 103–110 (1997)Google Scholar
  2. 2.
    Yin, L., Ning, X., Zhang, X.: Development and application of automatic fingerprint identi-fication technology. Journal of Nanjing University(Natural Science) 38(1), 29–35 (2002)Google Scholar
  3. 3.
    Hong, L., Wan, Y., Jain, A.K.: Fingerprint image enhancement: algorithm and perform-ance evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 777–789 (1998)CrossRefGoogle Scholar
  4. 4.
    Hung, D.C.D.: Enhancement feature purification of fingerprint images. Pattern Recognition 26(11), 1661–1671 (1993)CrossRefGoogle Scholar
  5. 5.
    Maio, D., Maltoni, D.: Ridge-line density estimation in digital images. In: Proceedings of 14th International Conference on Pattern Recognition, Brisbane, Australia, pp. 534–538 (1998)Google Scholar
  6. 6.
    Lin, W.C., Dubes, R.C.: A review of ridge counting in dermatoglyphics. Pattern Rec-ognition 16(2), 1–8 (1983)CrossRefGoogle Scholar
  7. 7.
    O’Gorman, L., Neckerson, J.V.: An approach to fingerprint filter design. Pattern Recognition 22(1), 29–38 (1989)CrossRefGoogle Scholar
  8. 8.
    Kovacs-Vajna, Z.M., Rovatti, R., Frazzoni, M.: Fingerprint ridge distance computa-tion methodologies. Pattern Recognition 33, 69–80 (2000)CrossRefGoogle Scholar
  9. 9.
    Chen, Y., Yin, Y., Zhang, X., et al.: A method based on statistics window for ridge distance estimation. Journal of image and graphics 8(3), 266–270 (2003)Google Scholar
  10. 10.
    Yin, Y., Wang, Y., Yu, F.: A method based on region level for ridge distance estimation. Chinese computer science 30(5), 201–208 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xiaosi Zhan
    • 1
  • Zhaocai Sun
    • 2
  • Yilong Yin
    • 2
  • Yayun Chu
    • 1
  1. 1.Computer DepartmentFuyan Normal CollegeFuyangChina
  2. 2.School of Computer Science & TechnologyShandong UniversityJinanChina

Personalised recommendations