Cover
Vol. 2 No. 1 (2006)

Published: July 31, 2006

Pages: 89-102

Original Article

FINGERPRINTS IDENTIFICATION USING NEUROFUZZY SYSTEM

Emad S. Jabber 1 Maytham A. Shahed
1Department of Computer Science, College of Science, University of Basrah, Basrah, Iraq
History
  • Received: May 31, 2006
  • Available Online: July 31, 2006
DOI

https://doi.org/10.37917/ijeee.2.1.10

Abstract

This paper deals with NeuroFuzzy System (NFS), which is used for fingerprint identification to determine a person's identity. Each fingerprint is represented by 8 bits/pixel grayscale image acquired by a scanner device. Many operations are performed on input image to present it on NFS, this operations are: image enhancement from noisy or distorted fingerprint image input and scaling the image to a suitable size presenting the maximum value for the pixel in grayscale image which represent the inputs for the NFS. For the NFS, it is trained on a set of fingerprints and tested on another set of fingerprints to illustrate its efficiency in identifying new fingerprints. The results proved that the NFS is an effective and simple method, but there are many factors that affect the efficiency of NFS learning and it has been noticed that the changing one of this factors affects the NFS results. These affecting factors are: number of training samples for each person, type and number of membership functions, and the type of fingerprint image that used.

Keywords

References

  1. D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, "Handbook of Fingerprint Recognition", Springer Verlag, New York, 2003.
  2. A. K. Jain and L. Hong, On-Line Fingerprint Verification", Technical Report MSU-CPS-99-19, Department of Computer Science, Michigan State University, East Lansing, Michigan, 1999.
  3. H. C. Lee and R. E. Gaensslen, editors, "Advances in Fingerprint Technology", New York: Elsevier, 1991.
  4. Federal Bureau of Investigation (F.B.I.), (www.fbi.gov/hq/cjisd/iafis.com).
  5. N. K. Ratha, A. Senior and R. M. Bolle, "Automated Biometrics", Lecture Notes in Computer Science, 2013:445-455, 2001.
  6. Federal Bureau of Investigation. "The Science of Fingerprints: Classification and Uses", U. S. Government Printing Office. Washington D. C., 1984.
  7. N. K. Ratha, S. Chen and A. K. Jain, "Adaptive Flow Orientation Based Feature Extraction in Fingerprint Images", Technical Report MSU-CPS-99-17, Department of Computer Science, Michigan State University, East Lansing, Michigan, 1999.
  8. W.F. Leung, S. H. Leung, W. H. Lau and A. Luk, "Fingerprint Recognition Using Neural Network", In IEER workshop Neural Network for Signal Processing, pages 226-235, 1991.
  9. H. H. Ammar, S. Zeng, and Z. Miao, "Parallel processing and fingerprint image comparison", Int. J. modeling and simulation, vol. 18, no. 2, pp 85-99, 1998.
  10. M. A. U. Khan, M. K. Khan, and M. A. Khan, "Fingerprint image enhancement using decimation-free directional filter bank", Information Technology Journal, vol. 7, no. 1, pp 16-20, 2005.
  11. S. G. Tzafestas and K. D. Blekas, "Hybrid Soft Computing Systems: A Critical Survey with Engineering Applications", (www.isda2001.soficomputing.net), 2001.
  12. J. Jantzen, "NeuroFuzzy Modelling", (www.iau.dtu.dk/~jj/pubs), 1998.
  13. S. Mitaim and B. Kosko, "The Shape of Fuzzy Sets in Adaptive Function Approximation", IEEE Trans. On Fuzzy Systems, vol.9, no.4, (www.sipi.usc.edu/~kosko/JEEETFS2001 , 2001.
  14. S. K. Oh, J. J. Lec, C. H. Park and B. R S. Kim, New fingerprint image enhancement using directional filter bank, WSCG. 11, 2003.