Supported by the National Natural Science Foundation of China under Grant Nos.60875018, 60575007 (国家自然科学基金); the Chinese Academy of Sciences Hundred Talents Program (中国科学院百人计划)
Fast Fingerprint Identification Based on Neighborhood Structure Around Singular Point
Combining the classification and matching of fingerprints together, a neighborhood structure is proposed in this paper, which includes the orientation field and minutia around the reference singular point. This structure has the advantage that the identification information is centralized around the singular point, and can dramatically decrease the calculation of matching. It can also be directly used as pattern in both the continuous classification and the fast matching of fingerprints, and carry out the fast identification of the large scale database. Experimental results on NIST and FVC2004 databases show that this algorithm can highly speed up the matching of large scale fingerprint database with a preferable performance, and it can be used in one-to-many matching of on-line fingerprint identification system.
[1] Maltoni D, Maio D, Jain AK, Prabhakar S. Handbook of Fingerprint Recognition. New York: Springer-Verlag, 2003. 173-202.
[2] Chang JH, Fan KC. A new model for fingerprint classification by ridge distribution sequences. Pattern Recognition, 2002,35(6): 1209-1223.
[3] Tan XJ, Bhanu B, Lin YQ. Fingerprint classification based on learned features. IEEE Trans. on Systems, Man, and Cybernetics, 2005,35(3):287-300.
[4] Lumini A, Maio D, Maltoni D. Continuous versus exclusive classification for fingerprint retrieval. Pattern Recognition Letters, 1997,18(10):1027-1034.
[5] Cappelli R, Maio D, Maltoni D. Combining fingerprint classifiers. In: Josef K, Fabio R, eds. Proc. of the Multiple Classifier Systems. Berlin: Springer-Verlag, 2000. 351-361.
[6] Boer JD, Bazen AM, Gerez SH. Indexing fingerprint database based on multiple features. In: Proc. of the ProRISC Workshop on Circuits, Systems and Signal Processing. 2001. http://utelnt.el.utwente.nl/links/gerez/publications/pslist.html
[7] Bhanu B, Tan XJ. Fingerprint indexing based on novel features of minutiae triplets. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2003,25(5):616-622.
[8] Li J, Wang H. Fingerprint indexing based on symmetrical measurement. In: Tang YY, Wang SP, Lorette G, Yeung DS, Yan H, eds. Proc. of the 18th Int’l Conf. on Pattern Recognition. Washington: IEEE Computer Society, 2006. 1038-1041.
[9] NIST Special Database 4, Fingerprint Database. 1992. http://www.nist.gov/srd/nistsd4.htm
[11] Hong L, Wan YF. Jain AK. Fingerprint image enhancement: Algorithm and performance evaluation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1998,20(8):777-789.
[12] Luo XP, Tian J. Image enhancement and minutia matching algorithms in automated fingerprint identification system. Journal of Software, 2002,13(5):946-956 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/13/946.pdf
[13] Kawagoe M, Tojo A. Fingerprint pattern classification. Pattern Recognition, 1984,17(3):295-303.
[14] Liu MH, Jiang XD, Kot AC. Reference point detection for fingerprint recognition. In: Zhang D, Jian AK, eds. Biometric Authentication. Berlin: Springer-Verlag, 2004. 272-279.
[15] Lee D, Choi K, Kim J. A robust fingerprint matching algorithm using local alignment. In: Kasturi R, Laurendeau D, Suen C, eds, Proc. of the 16th Int’l Conf. on Pattern Recognition, Vol.3. Washington: IEEE Computer Society, 2002. 803-806.
[16] Chen H, Tian J. A fingerprint matching algorithm with registration pattern inspection. Journal of Software, 2005,16(6):1046-1053 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/16/1046.htm
[17] Jiang XD, Yau WY. Fingerprint minutiae matching based on the local and global structures, In: Sanfeliu A, Villanueva JJ, Vanrell M, Alquezar R, Crowley J, Shirai Y. eds. Proc. of the 15th Int’l Conf. on Pattern Recognition. Washington: IEEE Computer Society, 2000. 1038-1041.
[18] Zhang Q, Yan H. Fingerprint classification based on extraction and analysis of singularities and pseudo ridges. Pattern Recognition, 2004,37(11):2233-2243.
[19] Yao Y, Frasconi P, Pontil M. Fingerprint classification with combination of support vector machines. In: Josef B, Fabrizio S, eds, Audio- and Video-Based Biometric Person Authentication. Berlin: Springer-Verlag, 2001. 253-25.
[20] Park CH, Park H, Fingerprint classification using fast fourier transform and nonlinear discriminant analysis. Pattern Recognition, 2005,38(4):495-503.
[21] Cappelli R, Maio D, Maltoni D. Fingerprint classification based on multi-space KL. In: Proc. of the Workshop on Automatic Identification Advanced Technologies. 1999. 117-120. http://biolab.csr.unibo.it/Bibliography.asp