The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Fingerprint Recognition using Gabor Filter

Gabor 필터를 이용한 지문 인식

  • Published : 2002.10.01
Download PDF
⟨ Previous Next ⟩

Abstract

Fingerprint recognition is a task to find a matching pattern in a database for a specific persons fingerprint. To accomplish this task, preprocessing, classification, and matching steps are taken for a large-scale fingerprint database but only the matching step is taken without classification for a small-scale database. The primary matching method is based on minutiae (ridge ending point, bifurcation). This matching method, however, requires a very complex computation to extract minutiae and match minutiae-to-minutiae accurately due to translation, rotation, nonlinear deformation of fingerprint and occurrence of spurious minutiae. In addition, this method requires a laborious preprocessing step in order to improve the quality of fingerprint Images. This paper proposes a new simple method to eliminate these problems. With this method, Gabor variance is used instead of minutiae for fingerprint recognition. The Gabor variance is computed from Gabor features that result from filtering a fingerprint image through Gabor filter. In this paper, this method is described and its test result is shown, demonstrating the potential of using this new method for fingerprint recognition.

지문인식은 입력지문이 데이터베이스 내에 있는 특성인의 지문과 일치하는지 여부를 확인하는 것이다. 이를 위해 대형 지문 데이터베이스에서는 여러 가지 전처리 과정과 분류 및 매칭을 하고 소형 지문데이터 인식에서는 분류를 하지 않고 바로 매칭을 한다. 매칭 방법은 특징점 (단점, 분기점)에 기초한 매칭이 주를 이루고 있는데, 특징점에 기초한 매칭은 지문의 변환, 회전, 비선형 변형, 가짜 특징점 등이 발생하는 문제로 특징점 추출 및 특징점들 간의 정확한 매칭에 매우 복잡한 계산을 필요로 하고, 지문의 품질향상을 위해 많은 전처리 과정이 필요한 문제점이 있다. 본 논문에서는 이러한 문제점을 해결하기 위하여 지문인식에 특징점을 이용하지 않고, Gabor 필터에 지문을 통과시켜 얻은 지문의 융선에서 Gabor 특징값을 산출하여 이 특징값을 지문인식에 이용하는 간단한 새로운 방법을 제안하고 이 방법이 지문인식 실행에 가능성을 가지고 있음을 실험으로 증명하였다.

Keywords

References

  1. A. K. Jain, L. Hong and R. Bolle, 'On-line Fingerprint Verification,' IEEE Trans. Pattern Analysis Machine Intelligence, Vol.19, No.4, pp.302-314, 160-166, 1998 https://doi.org/10.1109/34.587996
  2. E. R. Henry, 'Classification and Uses of Fingerprint,' London : Rout-Ledge, 1900
  3. A. Wahab and S. H. Chin, 'Novel Approach to Automated Fingerprint Recognition,' IEEE Proceeding-Vision, Image and Signal Processing, Vol.145, No.3, pp.160-166, 1998 https://doi.org/10.1049/ip-vis:19981809
  4. L. Hong, Y. Wan and A. K. Jain 'Fingerprint Image Enhancement Algorithms and Performance Evaluation,' Proc, 14th Int'l Conf. Pattern Recognition. Brisbane, pp.1373-1375, Aug., 1998
  5. A. K. Jain, S. Parbhakar, L. Hong, 'A Multichannel Approach to Fingerprint Classification,' IEEE Trans on Pattern Analysis and Machine Intelligence, Vol.21, No.4, 1999 https://doi.org/10.1109/34.761265
  6. B. Miller, 'Vital Signs of Identity,' IEEE Spectrum, Vol.31, No.2, pp.22-30, Feb., 1994 https://doi.org/10.1109/6.259484
  7. A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, 'A Identity Authentication System Using Fingerprints,' Processing of the IEEE, Vol.85, No.9, pp. 1365-1388, 1997 https://doi.org/10.1109/5.628674
  8. G. T. Gandela, P. J. Grother, C.I Watson, R. A. Wilkinson and C. L. Wilson 'PCASYS-A Pattern Level Classification Automation System for Fingerprints,' Technical Report NISTIR 5647, Apr., 1995
  9. N. K. Ratha, 'A Real-Time Matching System for Large Fingerprint Database,' IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol.18, No.8, Aug., 1996 https://doi.org/10.1109/34.531800
  10. L. Hong, Y. Wan and A. K. Jain, 'Fingerprint Image Enhancement : Algorithms and Performance Evaluation,' Proc. IEEE Comp. SOC. Workshop on Empirical https://doi.org/10.1109/34.709565
  11. B. Moayer and K. S. Fu, 'A Syntactic Approach to Fingerprint Pattern Recognition,' Pattern Recognition, Vol.7, 991-23, 1975 https://doi.org/10.1016/0031-3203(75)90011-4
  12. J. H. Wegstein, 'An Automated Fingerprint Identification System,' Technical Report 500-89. National Bureau of Standards, Bethesda, Md, 1982
  13. D. K. Isenor and S. G. Zaky, 'Fingerprint Identification Using Graph Matching,' Pattern Recognition, Vol.19, No.2. pp. 113-122. Nov., 1993 https://doi.org/10.1016/0031-3203(86)90017-8
  14. 'Application Briefs Computer Graphics in the Detective Business,' IEEE Computer Graphics and Applications. Vol. 5, No.4 pp.14-17, Apr., 1995 https://doi.org/10.1109/MCG.1985.276451
  15. J. D. Stose, L. A. Alyea : 'Automated System for Fingerprint Authentication Using Pores and Ridge Structure' Department of Defence 9800 Sauoge Road, Ft. Meade, MD 20755-6000, 301-688-0726
  16. C. J. Lee and S. D. Wang, 'Fingerprint Feature Extraction Using Gabor Filter,' Electronics Letters, Vol.35, No.4, pp. 288-290, 1999 https://doi.org/10.1049/el:19990213
  17. Y. Hamamoto, S. Uchimura, M. Watanabe, T. Yasuda, Y. Mitani and S. Tomita, 'A Gabor Filter Based Methode for Recognizing Hand Written Numerals,' Pattern Recognition, Vol.31, No.4, pp.395-400 https://doi.org/10.1016/S0031-3203(97)00057-5
  18. 경찰청 감식과, '십지 지문 분류 요령집
  19. 채종진, 박래홍, 'Ridge-Line을 이용한 계층적 지문 인식', 한국 정보과학회논문지, Vo1.18, No.5, September, 1991
  20. K. Karu and A. K. Jain, 'Fingerprint Classification,' Pattern Recognition. Vol.29, No.3, pp.389-404, 1996 https://doi.org/10.1016/0031-3203(95)00106-9
  21. L. Hong and A. K. Jain, 'Classification of Fingerprint Images,' Technical Report MSUCPS ; TR98-18 Michigan State Univ, June, 1998
  22. C. V. K. Rao and K. Black, 'Type Classification of Fingerprints : A Syntactic,' IEEE Trans, Pattern Analysis and Machine Intelligence, Vol.2, No.3, pp.223-231, 1980 https://doi.org/10.1109/TPAMI.1980.4767009