Matching Algorithms using the Union and Division

결합과 분배를 이용한 정합 알고리즘

  • Published : 2004.08.01


Fingerprint Recognition System is made up of Off-line treatment and On-line treatment; the one is registering all the information of there trieving features which are retrieved in the digitalized fingerprint getting out of the analog fingerprint through the fingerprint acquisition device and the other is the treatment making the decision whether the users are approved to be accessed to the system or not with matching them with the fingerprint features which are retrieved and database from the input fingerprint when the users are approaching the system to use. In matching between On-line and Off-line treatment, the most important thing is which features we are going to use as the standard. Therefore, we have been using “Delta” and “Core” as this standard until now, but there might have been some deficits not to exist in every person when we set them up as the standards. In order to handle the users who do not have those features, we are still using the matching method which enables us to make up of the spanning tree or the triangulation with the relations of the spanned feature. However, there are some overheads of the time on these methods and it is not sure whether they make the correct matching or not. Therefore, I would like to represent the more correct matching algorism in this paper which has not only better matching rate but also lower mismatching rate compared to the present matching algorism by selecting the line segment connecting two minutiae on the same ridge and furrow structures as the reference point.


  1. Garfinkel, Simson, and Gene Spafford. Practical Unix and Internet Security. O'Reilly & Associates, Inc., April 1996
  2. Gollmann, Dieter. Computer Security. John Wiley and Sons, August 1999
  3. B. Moayer, K. S. Fu, 'A syntactic approach to fingerprint pattern recognition', Pattern Recognition 7, 1-23, 1975
  4. D. K. Isenor, S. G. Zaky, Fingerprint identificationusing graph matching, pattern Recognition 19. 113-122, 1986
  5. Q. Xiao, H. Rafat, A combined statistical and structural approach for fingerprint image postprocessing proceedings of the IEEE International Conference on systems, Man and Cybernetics Conference, pp. 331-335, 1990
  6. The Science of Fingerprints: Classification and Uses United States Department of justice, Federal Bureau of Investigation, Washington, rev. 12-84, 1988
  7. A. shimizu, M. Hase. Tmas. Inst. Electronic Comm. Engineers Japan, Part D, J67D(5), pp. 627
  8. A. Farina, Z. M. Kovacs-vajna, Alverto Leone, 'Fingerprint minutiae extraction from slceletonixed binary images', Pattern Recognition, vol. 32, no. 4, pp. 877-889, 1999.
  9. An drew K. Hrechak, James A. Mchugh, 'Automated Fingerprint Recognition using structural matching', Pattern Recognition, vol. 23, pp. 893-904, 1990
  10. F. Galton, Finger Prints, MacMillan, London, 1892
  11. W. C. Lin, R. C. Dubes, A review of ridge counting in dermatoglyphics, Pattern Recognition 16, 1-8, 1983
  12. L. Coetzee and E. C. Botha, 'Fingerprint Recognition in Low Quality Images,' Pattern Recognition, vol. 26, no. 10, pp. 1441-1460, 1993
  13. L. Wang and T. Pavlidis, 'Direct Gray Scale Extraction of Features for Character Recognition,' IEEE Trans. Pattern Analysis Machine Intelligence, vol. 15, no. 10, pp. 1053-1067, 1993
  14. C. I. Watson and C. L. Wilson, 'Detection of Curved and Straight Segments from 1107 Gray Scale Topography,' Image Understanding, vol. 58, no. 3, pp. 352-365, 1993
  15. D. Mario and D. Maltoni, 'Direct Gray-Scale Minutiae Detection In Fingerprints,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 1, pp. 27-40, 1997
  16. X. Jiang, W. Y. Yau, and W. Ser, 'Detecting the Fingerprint minutiae by adaptive tracing the gray-level ridge,' Pattern Recognition 34, pp. 999-1013, 2000