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Reseach Article

Robust Fingerprint Matching

by Ovais Ismail, Bhawani Singh Shekhwat
journal cover thumbnail
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 21
Year of Publication: 2010
Authors: Ovais Ismail, Bhawani Singh Shekhwat
10.5120/60-651

Ovais Ismail, Bhawani Singh Shekhwat . Robust Fingerprint Matching. International Journal of Computer Applications. 1, 21 ( February 2010), 40-43. DOI=10.5120/60-651

@article{ 10.5120/60-651,
author = { Ovais Ismail, Bhawani Singh Shekhwat },
title = { Robust Fingerprint Matching },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 21 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 40-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number21/60-651/ },
doi = { 10.5120/60-651 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:47:32.176199+05:30
%A Ovais Ismail
%A Bhawani Singh Shekhwat
%T Robust Fingerprint Matching
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 21
%P 40-43
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Pattern recognition aims to classify data (patterns) based on a priori knowledge or on statistical information extracted from the patterns. Under fingerprint recognition we have chosen the Fingerprint Matching as the research work. Most automatic systems for fingerprint comparison are based on minutiae matching. Minutiae are essentially terminations and bifurcations of the ridge lines that constitute a fingerprint pattern. A minutia extraction approach has been presented using the midpoint ridge contours [17]. The technique conferred in this paper is based on the extraction of minutiae from the thinned, binarized and segmented version of a fingerprint image. This paper describes fingerprint matching by using occurrences of minutiae points which enhances the performance of the matching algorithm. The quality of the proposed algorithm is that it can be associated with any algorithm as a part of pattern matching.

References
  1. G. Parziale and E. Diaz-Santana. “The surround imager: A multi-camera touch-less device to acquire 3d rolledequivalent fingerprints”, in Proc. of IAPR Int. Conf. On Biometrics, LNCS, volume 3832, pages pp.244–250, 2006.
  2. L. Jinxiang, H. Zhongyang, and C. Kap Luk. Direct minutiae extraction from gray-level fingerprint image by relationship examination. In International Conference on Image Processing(ICIP), volume 2, pages 427–430 vol.2, 2000.
  3. D. Maio and D. Maltoni. Direct gray-scale minutiae detection in fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(1):27–40, 1997.
  4. B. Bir and T. Xuejun. “Fingerprint indexing based on novel features of minutiae triplets”. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(5):616–622, 2003.
  5. J. Xudong and Y. Wei-Yun. “Fingerprint minutiae matching based on the local and global structures”. In Proc. Of International Conference on Pattern Recognition (ICPR), volume 2, pages 1038–1041 vol.2, 2000.
  6. B. Bir and T. Xuejun. “Fingerprint indexing based on novel features of minutiae triplets”. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(5):616–622, 2003.
  7. S. Prabhakar, A. K. Jain, and S. Pankanti. Learning fingerprint minutiae location and type. Pattern Recognition, 36(8):1847–1857, 2003.
  8. S. A.M.Bazen, M. Van Otterlo and M. Poel. “Areinforcement learning agent for minutiae extraction from fingerprints”. In Proc. BNAIC, pages pp.329–336, 2001.
  9. A. K. Jain, L. Hong, and B. R. “On-line fingerprint verification”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4):302–314, 1997.
  10. Adrian Lim Hooi Jin et al, “Fingerprint Identification and Recognition Using Backpropagation Neural Network”, Students Conference on Research and Development Proceedings, IEEE, 2002
  11. A. K. Jain, S. Prabhakar, L. Hong, S. Pankanti, Filterbankbased fingerprint matching, IEEE Transactions on Image Processing 9(5)(2000), 846-859.
  12. L. Hong, Y. Wan, A. K. Jain, Fingerprint image enhancement: algorithm and performance evaluation, IEEE Trans. Pattern Analalysis and Machine Intelligence, 20(8)(1998) 777-789.
  13. J. Hollingum, ”Automated Fingerprint Analysis Offers Fast Verification,” Sensor Review, vol. 12, no. 3, pp. 12-15,1992.
  14. B.M. Mehtre and N.N. Murthy, “A Minutia Based Fingerprint Identification System,” Proc. Second Int’l Conf. Advances in Pattern Recognition and Digital Techniques, Calcutta 1986.
  15. American National Standards Institute, Fingerprint Identification- Data Format for Information Interchange. New York, 1986.
  16. Govindaraju, Zhixin Shi, “Feature Extraction Using a Chaincoded Contour Representation of Fingerprint Images”, EDAR, Department of Computer Science and Engineering, New York 14226 March 24, 2003
  17. Bhupesh Gour, Krishna Singh, “Fast Fingerprint Identification System Using Minutiae Matching with Back Propagation Neural Network and Self-Organizing Map.”, Proceedings of GLOGIFT, Bhopal, December, 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Minutiae Extraction Segmentation Contrast Enhancement Midpoint Ridge Contour