Robust Reference Point and Feature Extraction Method for Fingerprint Verification using Gradient Probabilistic Model

지문 인식을 위한 Gradient의 확률 모델을 이용하는 강인한 기준점 검출 및 특징 추출 방법

  • 박준범 (고려대학교 전자컴퓨터공학과) ;
  • 고한석 (고려대학교 전자컴퓨터공학과)
  • Published : 2003.11.01

Abstract

A novel reference point detection method is proposed by exploiting tile gradient probabilistic model that captures the curvature information of fingerprint. The detection of reference point is accomplished through searching and locating the points of occurrence of the most evenly distributed gradient in a probabilistic sense. The uniformly distributed gradient texture represents either the core point itself or those of similar points that can be used to establish the rigid reference from which to map the features for recognition. Key benefits are reductions in preprocessing and consistency of locating the same points as the reference points even when processing arch type fingerprints. Moreover, the new feature extraction method is proposed by improving the existing feature extraction using filterbank method. Experimental results indicate the superiority of tile proposed scheme in terms of computational time in feature extraction and verification rate in various noisy environments. In particular, the proposed gradient probabilistic model achieved 49% improvement under ambient noise, 39.2% under brightness noise and 15.7% under a salt and pepper noise environment, respectively, in FAR for the arch type fingerprints. Moreover, a reduction of 0.07sec in reference point detection time of the GPM is shown possible compared to using the leading the poincare index method and a reduction of 0.06sec in code extraction time of the new filterbank mettled is shown possible compared to using the leading the existing filterbank method.

본 논문에서는 지문인증 시스템에서 인증 성능을 향상시키기 위한 기준점 검출 알고리즘과 특징 추출에 있어서 새로운 filterbank방법을 제안한다. 제안한 기준점 검출 알고리즘 GPM(Gradient Probabilistic Method)은 4개의 방향성분을 추출하여 방향성분을 가장 균일하게 가지는 지점을 검출하는 방법이며, 기존의 Poincare index방법과 달리 수학적 통계적 방법을 사용하기 때문에 지문의 융선에 대한 세부적이고 세밀한 전처리 과정이 불필요하며, arch형태 지문의 기준점 검출에 대한 단점을 해결한다. 또한, 제안한 filterbank방법은 기존filterbank방법에서 특징의 불균일한 분포로 생기는 단점을 균일한 분포로 만들어 추출함으로써 해결한다. 제안한 GPM의 실험결과 기존의 Poincare index방법에 비해서, 일반환경뿐 아니라 잡음환경에서의 특징 추출 시간과 인증률에서 우수함을 보여준다. 특히, 제안한 GPM은 Poincare index방법에 비해서, arch type의 지문에 대한 FAR은 일반 환경에서 49%, 밝기 잡음환경에서 39.2%, salt and pepper 잡음환경에서 15.7%의 향상을 보여준다. 또한, 기준점 검출시간에 있어서, 제안한 GPM방법은 기존의 Poincare index방법보다 0.07초의 감소를 보여주며, 특징추출 시간에 있어서도 제안한 filterbank 알고리즘은 기존의 filterbank 방법에 비해서 0.06sec의 감소를 보여준다.

Keywords

References

  1. A. K. Jain, L. Hong, and R. Bolle, 'On-Line Fignerprint Verification,' in IEEE Transactions on Pattrn Analysis and Machine Intelligence, vol. 19, no. 4, pp. 302-313, April 1997 https://doi.org/10.1109/34.587996
  2. Lin Hong, Yifei Wan, Anil Jain, 'Fingerprint Image Enhancement: Algorithm and Performance Evaluation' in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 777-789, August, 1998 https://doi.org/10.1109/34.709565
  3. Kalle Karu and Anil K. Jain, 'Fingerprint Classification,' in Pattern Recognition, vol. 29, no. 3, pp. 389-404, 1996 https://doi.org/10.1016/0031-3203(95)00106-9
  4. Anil K. Jain, Salil Prabhakar, and Lin Hong, 'A Multichannel Approch to Fingerprint Classification,' in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no.4, pp. 348-359, April 1999 https://doi.org/10.1109/34.761265
  5. Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification, John Wiley & Sons, 2001
  6. Y. Hamamoto, S. Uchimura, M. Watanabe, T. Yasuda, Y. Mitani and S. Tomita, 'A Gabor filter-based method for recognizing handwitten numerals,' in Pattern Recognition, Vol. 31, No. 4, pp. 395-400, 1998 https://doi.org/10.1016/S0031-3203(97)00057-5
  7. Anil. K. Jain, Salil Prabhakar, Lin Hong, and Sharath Pankanti, 'Filterbank-based Fingerprint Matching,' IEEE Tansactions on Image Processing, vol. 9, no. 5, pp. 846-859, May 2000 https://doi.org/10.1109/83.841531
  8. Chih-Jen Lee, Sheng-De Wang, and Kuo-Ping Wu, 'Fingerprint Recognition Using Principal Gabor Basis Function,' in Proceedings of 2001 Intermational Symposium on Intelligent Multimeda, Video and Speech Processing, pp. 393-396, May 2-4 2001, Hong Kong https://doi.org/10.1109/ISIMP.2001.925416
  9. Ani K. Jain, Lin Hong, Sharath Pankanti, and Ruud Bolle, 'An Identity-Authentication System Using Fingerprints,' in Proceedings of the IEEE, vol. 85, no. 9, pp. 1365-1204, September 1997 https://doi.org/10.1109/5.628674