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Reseach Article

An Efficient Fingerprint Matching System for Low Quality Images

by Zin Mar Win, Myint Myint Sein
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 26 - Number 4
Year of Publication: 2011
Authors: Zin Mar Win, Myint Myint Sein
10.5120/3094-4246

Zin Mar Win, Myint Myint Sein . An Efficient Fingerprint Matching System for Low Quality Images. International Journal of Computer Applications. 26, 4 ( July 2011), 5-12. DOI=10.5120/3094-4246

@article{ 10.5120/3094-4246,
author = { Zin Mar Win, Myint Myint Sein },
title = { An Efficient Fingerprint Matching System for Low Quality Images },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 26 },
number = { 4 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 5-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume26/number4/3094-4246/ },
doi = { 10.5120/3094-4246 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:11:55.405128+05:30
%A Zin Mar Win
%A Myint Myint Sein
%T An Efficient Fingerprint Matching System for Low Quality Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 26
%N 4
%P 5-12
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fingerprint-based identification is one of the most well-known and publicized biometrics for personal identification. It remains a reliable, efficient and commonly accepted biometric. In this paper, a fingerprint recognition system for identifying the low quality fingerprint images on Myanmar National Registration Cards (NRCs) is developed. Traditional minutia based approach is not robust to poor quality fingerprint images. In proposed system, ridge feature-based approach for fingerprint recognition using contextual filter and single pass thinning algorithm is developed. The input image is preprocessed and gabor filtering is applied for ridge line enhancement. The system extracts the ridge line features from the skeleton image derived from single pass thinning algorithm and it is compared to the database using Euclidean distance metric. The effectiveness of the proposed system can be confirmed through the experimental results.

References
  1. Prabhakar, S., Pankanti, S. and Jain A.K. 2003.Biometrics recognition: security and privacy concern. IEEE Security&Privacy Magazine 1, 33-42.
  2. Jain, A. K., Yi C. and Demirkus, M. Jan 2007. Pores and ridges: High resolution fingerprint matching using Level 3 features. IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 29, no.1, pp. 1-13.
  3. Hong, L. and Jain, A.K.1999. Classification of fingerprint images. 11th Scandinavian Conference on Image Analysis.
  4. Kawagoe, M. and Tojo,A. 1984. Fingerprint pattern classification. Pattern Recognition, pp.295–303.
  5. Jain, A.K., Prabhakar, S., Hong, L. and Pankanti, S.2000. Filterbank-based fingerprint matching. IEEE Trans. Image Process, pp.846–859.
  6. Jain A.K. and Pankanti, S.2000. Fingerprint classification and matching, in: Handbook for Image and Video Processing. Academic Press.
  7. Nilsson, K. and Bigun, J. 2002. Complex filters applied to fingerprint images detecting prominent symmetry points used for alignment. Biometric Authentication, in: LNCS, vol. 2359, Springer-Verlag, Berlin, Heidelberg, pp. 39–47.
  8. Yang, J.C. and Park, D.S. 2008, A fingerprint verification algorithm using tessellated invariant moment features. Neurocomputing, pp.1939–1946.
  9. Jain A.K. and Feng, J. 2010. Latent Fingerprint Matching. IEEE Transs. on Pattern Analysis and Machine Intelligence.
  10. Ito, K., Morita, A., Aoki, T., Higuchi, T., Nakajima, H. and Kobayashi, K.2005. A Fingerprint Recognition Algorithm Using Phase-Based Image Matching for Low-Quality Fingerprints. IEEE Int’l Conf. on Image Processing, vol.II.
  11. LinLin, S. and Alex, K. 2009.A New Wavelet Domain Feature for Fingerprint Recognition. Biomedical Soft Computing and Human Sciences, Vol.14, No.1, pp.55-59.
  12. Tico, M., Kuosmanen, P. and Saarinen, J. 2001.Wavelet Domain Features for Fingerprint Recognition. Electronic Letters, Vol. 37, No. 1, pp. 21-22.
  13. He, Y., Tian, J., Luo, X. and Zhang, T. 2003. Image Enhancement and Minutia Matching in Fingerprint Verification, Pattern Recognition Letter, Vol.24, No. 9-10, pp. 1349-1360.
  14. Maio, D. and Maltoni, D. 1997. Direct Gray-Scale Minutiae Detection in Fingerprints. IEEE Transaction on PAMI, Vol. 19, No. 1, pp.17-40.
  15. Kamijo M. 1993. Classifying Fingerprint Images using Neural Network: Deriving the Classification State, IEEE International Conference on Neural network, vol.3, pp. 1932-1937.
  16. Karu K., and Jain A.K. 1996. Fingerprint Classification. Pattern Recognition, vol. 29, no. 3, pp. 389-404.
  17. Dadgostar, M., Tabrizi, P. R., Fatemizadeh, E. and Soltanian Z. H.2009.Feature Extraction using Gabor Filter and Recursive Fisher Linear Discriminant with Application in Fingerprint Identification. Seventh International Conference on Advances in Pattern Recognition, pp. 217-220.
  18. Umair, M. K., Shoab, A. K., Naveed E. and Riaz U. R. 2009.A Fingerprint Verification System using Minutiae and Wavelet Based Features. International Conference on Emerging Technologies, pp. 291-296.
  19. Zhou, W., Han, J., Zeng, X., and Yan, W. 2009.Fingerprint Verification Based on Wavelet and Edge Detection. NinthInternational Conference on Electronic Measurement and Instruments, pp. 1001– 1004.
  20. Leon, Sanchez, J., Aguilar, G., Toscano, G., Perez, L. and Ramirez, H. 2009.Fingerprint Verification Applying Invariant Moments. Fifty Second IEEE International Midwest Symposium on Circuits and Systems, pp. 751 – 757.
  21. Bhowmik, U. K., Ashrafi, A., and Adhami, R. R. 2009. A Fingerprint Verification Algorithm using the Smallest Minimum Sum of Closest Euclidean Distance. International Conference on Electrical Communications and Computers, pp. 90 -95.
  22. Helfroush, M.S. and Mohammadpour, M. 2009.Fingerprint Verification System: A Non-Minutiae Based Approach. International Conference on Computer, Control and Communication, pp. 1 – 4.
  23. Paulino, A. A., Jain, A. K. and Jianjiang, F., 2010. Latent Fingerprint Matching: Fusion of Manually Marked and Derived Minutiae. 23rd SIBGRAPI- Conference on Graphics, Patterns and Images
  24. Drets, G.A. and Liljenstrom, H.G.1999. Fingerprint subclassification: A neural network approach. Intelligent Biometric Techniques in Fingerprint and Face Recognition, pages 109–134.
  25. OGorman, L. and Nickerson, J.V. 1989. An approach to fingerprint filter design. Pattern Recognition, 22(1):29–38.
  26. Wilson, C.L., Candela, G.T. and Watson, C.I. 1994. Neuralnetwork fingerprint classification. Journal of Artificial Neural Networks, 1(2):203–228.
  27. Kass, M. and Witkin, A.1987. Analyzing oriented patterns. Computer Vision, Graphics, and Image Processing, 37(3):362–385.
  28. Hong, L., W. Y., and Jain. A. K.1998. Fingerprint image enhancement: Algorithm and performance evaluation. IEEE Trans. On Pattern Analysis and Machine Intelligence.
  29. Bazen, A.M. and Gerez, S.H.2002. Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Transaction On PAMI, 24(7):905–919.
  30. Ng, G. S., Zhou, R. W. and Quek, C. 1994. A Novel Single Pass Thinning Algorithm, IEEE Transaction on System Man and Cybernetics.
Index Terms

Computer Science
Information Sciences

Keywords

Fingerprint Fingerprint Recognition Gabor filter Single Pass thinning Histogram matching