Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지

Matching Algorithms using the Union and Division

결합과 분배를 이용한 정합 알고리즘

  • Published : 2004.08.01
Download PDF
⟨ Previous Next ⟩

Abstract

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.

지문 인식 시스템은 지문인식 장치(fingerprint acquisition device)를 통하여 아날로그(analog) 지문으로 부터 변환된 디지털(digital) 지문에서 특이점을 추출하고 추출한 특이점들에 대한 정보를 데이터 베이스에 등록하는 오프라인(off-line) 처리와 사용자가 시스템에 접근 할 때 입력한 지문으로부터 특이점을 추출한 후 데이터베이스에 저장되어 있는 특이점들과 정합(matching) 하여 사용자의 시스템 접근 여부를 결정하는 온라인(on-line) 처리로 이루어져 있다. 정합에 있어서 가장 중요한 것은 어떤 기준점을 어떻게 설정하느냐 하는 것이다. 지금까지 삼각주나 코어를 기준점으로 잡거나, 기준점으로 설정하는 경우에는 모든 사람에게 존재하지 않는 단점이 있다. 특징점이 없는 사용자들을 처리하기 위하여 특이점들의 상호 관계를 스패닝 트리를 구성하거나, 삼각화를 하여 정합 하는 방법들이 이용되고 있다. 그러나 이러한 방법들은 시간에 대한 오버헤드가 있고 정확하게 정합 한다는 보장을 하지 못한다. 본 논문에서는 동일 등선 줄기 상에 있는 두 특이점을 잇는 선분을 기준점으로 선택함으로서 기존 정합 알고리즘과 비교하여 인식률이 높고 오인식률이 낮으며 효율성 면에서도 우수하고 정확하게 정합 하는 알고리즘을 제안한다.

Keywords

References

  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 https://doi.org/10.1016/0031-3203(75)90011-4
  4. D. K. Isenor, S. G. Zaky, Fingerprint identificationusing graph matching, pattern Recognition 19. 113-122, 1986 https://doi.org/10.1016/0031-3203(86)90017-8
  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. https://doi.org/10.1016/S0031-3203(98)00107-1
  9. An drew K. Hrechak, James A. Mchugh, 'Automated Fingerprint Recognition using structural matching', Pattern Recognition, vol. 23, pp. 893-904, 1990 https://doi.org/10.1016/0031-3203(90)90134-7
  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 https://doi.org/10.1016/0031-3203(83)90002-X
  12. L. Coetzee and E. C. Botha, 'Fingerprint Recognition in Low Quality Images,' Pattern Recognition, vol. 26, no. 10, pp. 1441-1460, 1993 https://doi.org/10.1016/0031-3203(93)90151-L
  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 https://doi.org/10.1109/34.254062
  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 https://doi.org/10.1109/34.566808
  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 https://doi.org/10.1016/S0031-3203(00)00050-9