On Improving Interoperability of Fingerprint Recognition Using Resolution Compensation Based on Sensor Evaluation

  • Jihyeon Jang
  • Stephen J. Elliott
  • Hakil Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

The purpose of this paper is the development of a compensation algorithm by which the interoperability of fingerprint recognition can be improved among various different fingerprint sensors. In order to compensate for the different characteristics of fingerprint sensors, an initial evaluation of the sensors using both the ink-stamped method and the flat artificial finger pattern method was undertaken. Then the resulted image resolution was incorporated to the compensation algorithms. This paper proposes Common resolution method and Relative resolution method for compensating different resolutions of fingerprint images captured by disparate sensors. Both methods can be applied to image-level and minutia-level. This paper shows the results of the minutiae-level compensation. The Minutiae format adhered to the standard format established by ISO/IEC JTC1/SC37. In order to compensate the direction of minutiae in minutia-level, Unit vector method is proposed. The fingerprint database used in the performance evaluation is part of KFRIA-DB (Korea Fingerprint Recognition Interoperability Alliance Database) collected by the authors and supported by KFRIA. Before compensation, the average EER was 8.62% and improved to 5.37% by the relative resolution compensation and to 6.37% by the common resolution compensation. This paper will make a significant contribution to interoperability in the system integration using different sensors.

Keywords

Control Line Dynamic Time Warping Resolution Method Fingerprint Image Compensation Algorithm 
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.

References

  1. 1.
    Lee, B.G., Nam, J.W., Kim, H.I.: Evaluation Methodology for Fingerprint Sensors using Image. In: CISC 2005, vol. 15(1), pp. 9–12 (June 2005)Google Scholar
  2. 2.
    Nam, J.W., Kim, H.I.: Evaluation of Fingerprint Sensors using Confidence Interval. In: CISC 2005, vol. 15(2), pp. 527–530 (December 2005)Google Scholar
  3. 3.
    Text of CD 19795-4, Biometric Performance Testing and Reporting - Part 4: Performance and Interoperability Testing of Interchange Formats. Google Scholar
  4. 4.
  5. 5.
  6. 6.
    ISO/IEC JTC1/SC37 N927, Text of FDIS 19794-4, Biometric Data Interchange Formats -Part 4: Finger Image DataGoogle Scholar
  7. 7.
    ISO/IEC JTC1/SC37 N1488, Text of FDIS 19794-3, Biometric Data Interchange Formats - Part 3: Finger Pattern Spectral DataGoogle Scholar
  8. 8.
    ISO/IEC JTC1/SC37 N1567, Text of FDIS 19794-8, Biometric Data Interchange Formats - Part 8: Finger Pattern Skeletal DataGoogle Scholar
  9. 9.
    ISO/IEC JTC1/SC37 N945, Text of FDIS 19794-2, Biometric Data Interchange Formats -Part 2: Finger Minutiae DataGoogle Scholar
  10. 10.
    Bolle, R.M., Colville, S.E., Pankanti, S.U.: System and method for determining ridge counts in fingerprint image processing. U.S. Patent No. 6266433 (July 2001)Google Scholar
  11. 11.
    Kovacs-Vajna, Z.M.: A Fingerprint Verification System Based on Triangular Matching and Dynamic Time Warping. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11) (November 2000)Google Scholar
  12. 12.
    Germain, R.S., Califano, A., Colville, S.: Fingerprint Matching Using Transformation Parameter Clustering. IEEE Computational Science & Engineering (1997)Google Scholar
  13. 13.
    Ratha, N.K., Pandit, V.D., Bolle, R.M., Vaish, V.: Robust Fingerprint Authentication Using Local Structural Similarity. In: Proc. Workshop on Applications of Computer Vision, pp. 29–34 (December 4-6, 2000)Google Scholar
  14. 14.
    Yi, E., Jun, S., Ryu, C., Kim, H.: A Cross-Matching System for Various Types of Fingerprint Scanner. In: Proc. The 4th International Workshop on Information Security Information (2003)Google Scholar
  15. 15.
  16. 16.
  17. 17.
    Gomes, J., Darsa, L., Costa, B., Velho, L.: Warping and Morphing of Graphical Object. Morgan Kaufmann, San Francisco (1999)Google Scholar
  18. 18.

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jihyeon Jang
    • 1
  • Stephen J. Elliott
    • 2
  • Hakil Kim
    • 1
  1. 1.Graduate School of Information Technology & Telecommunication, Inha University 
  2. 2.Department of Industrial Technology, Purdue University 

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