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

박준범 (고려대학교 전자컴퓨터공학과)
고한석 (고려대학교 전자컴퓨터공학과)
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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.
Keywords
filterbank; Gabor-filter;
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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   DOI   ScienceOn
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   DOI   ScienceOn
3 Kalle Karu and Anil K. Jain, 'Fingerprint Classification,' in Pattern Recognition, vol. 29, no. 3, pp. 389-404, 1996   DOI   ScienceOn
4 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   DOI   ScienceOn
5 Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification, John Wiley & Sons, 2001
6 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   DOI   ScienceOn
7 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   DOI   ScienceOn
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   DOI
9 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   DOI   ScienceOn