An improved scheme to fingerprint classification

  • Weimin Huang
  • Jian-Kang Wu
Document Image Analysis and Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)

Abstract

An improved scheme to fingerprint classification is presented in this paper. It is well known that automatic fingerprint identification system usually needs to access very huge data — thousands of fingerprints stored in database. Thus a good automatic classification algorithm is an important module in such a system. Further more, for identification of fingerprint, it also asks few error occurred in its pre-classification stage. The scheme presented here extracts the singularities of fingerprints. Based on those points, the flow-lines which describe the global pattern of fingerprint are obtained with flow-line tracing algorithm using the direction and skeleton information. The properties of the flowlines are analyzed with respect to fingerprint pattern. Then the scheme performs the overlapped classification algorithm with these information, which reduced the error of classification greatly. Some experimental results are reported.

Keywords

Classification Pattern description Image processing Singular point Flow-line Fingerprint Uncertainty information 

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References

  1. 1.
    E. R. Henry. Classification and Uses of Finger Prints. London: Rouledge, 1900.Google Scholar
  2. 2.
    M. Kawagoe and A. Tojo. Fingerprint pattern classification. Pattern Recognition, 17:295–303, 1984.CrossRefGoogle Scholar
  3. 3.
    G. T. Candela et. al. PCASYS — A Pattern-Level Classification Automation System for Fingerprints. Technical Report NISTIR 5647, Computer Systems Laboratory, Advanced Systems Division, National Institute of Standards and Technology, U.S. Dept of Commerce, Aug. 1995.Google Scholar
  4. 4.
    K. Karu and A. K. Jian. Fingerprint Classification. Pattern Recognition, 29(3):389–404, 1996.CrossRefGoogle Scholar
  5. 5.
    U. Halici and G. Ongun. Fingerprint classification through self-organizing feature maps modified to treat uncertainties. Proceedings of the IEEE, 84(10):1497–512, 1996.CrossRefGoogle Scholar
  6. 6.
    Weimin Huang, Gang Rong and Zhaoqi Bian. A Method of the Description of Image Information for the Classification of Fingerprints. Journal of Tsinghua University, 34(54):107–115, 1994.Google Scholar
  7. 7.
    B. M. Mehtre, N. N. Murthy, S. Kapoor, and B. Chatterjee. Segmentation of fingerprint images using the directional image. Pattern Recognition, 20:429–435, 1987.CrossRefGoogle Scholar
  8. 8.
    Weimin Huang, Jian-Kang Wu and Chian-Prong Lam. Fingerprint Image Enhancement with Steerable Filters. Technical Report ISS97-06, Institute of Systems Science, National University of Singapore, July. 1997.Google Scholar
  9. 9.
    William T. Freeman and Edward H. Adelson. The design and use of steerable filters. IEEE Trans on Patt. Anal. Mach. Intell., 13(9):891–906, 1991.CrossRefGoogle Scholar
  10. 10.
    R. M. Haralick et. al. Image Segmentation Techniques. Comput. Vision Graphics Image Process., 29:100–132, 1985.Google Scholar
  11. 11.
    Louis Coetzee and Elizabeth C. Botha. Fingerprint Recognition in Low Quality Images. Pattern Recognition, 26(10):1441–1460, 1993.CrossRefGoogle Scholar
  12. 12.
    D. C. Douglas Hung. Enhancement and Feature Purification of Fingerprint Images. Pattern Recognition, 26(11):1661–1671, 1993.CrossRefGoogle Scholar
  13. 13.
    S.W. Lee L. Lam and C.Y. Suen. Thinning Methodologies — A Comprehensive Survey. IEEE Trans. on Pattern Analysis and Machine Intelligence, 14:869–885, 1992.CrossRefGoogle Scholar
  14. 14.
    C. V. K. Rao and K. Balck. Type classification of fingerprints: A syntactic approach. IEEE Trans. PAMI, 2:223–231, 1980.Google Scholar

Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • Weimin Huang
    • 1
  • Jian-Kang Wu
    • 1
  1. 1.Kent Ridge Digital LabsKent RidgeSingapore

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