International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Fingerprint Verification Based on Invariant Moment Features and Nonlinear BPNN

  • Yang, Ju-Cheng (Department of Cpmputer Science and Engineering, Hankuk University of Foreign Studies) ;
  • Park, Dong-Sun (Division of Electronics & Information Engineering, Chonbuk National University)
  • Published : 2008.12.31
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Abstract

A fingerprint verification system based on a set of invariant moment features and a nonlinear Back Propagation Neural Network(BPNN) verifier is proposed. An image-based method with invariant moment features for fingerprint verification is used to overcome the demerits of traditional minutiae-based methods and other image-based methods. The proposed system contains two stages: an off-line stage for template processing and an on-line stage for testing with input fingerprints. The system preprocesses fingerprints and reliably detects a unique reference point to determine a Region-of-Interest(ROI). A total of four sets of seven invariant moment features are extracted from four partitioned sub-images of an ROI. Matching between the feature vectors of a test fingerprint and those of a template fingerprint in the database is evaluated by a nonlinear BPNN and its performance is compared with other methods in terms of absolute distance as a similarity measure. The experimental results show that the proposed method with BPNN matching has a higher matching accuracy, while the method with absolute distance has a faster matching speed. Comparison results with other famous methods also show that the proposed method outperforms them in verification accuracy.

Keywords

References

  1. T. Amornraksa and S. Tachaphetpiboon, "Fingerprint recognition using DCT features," Electronics Letters, vol. 42, no. 9, pp. 522-523, 2006 https://doi.org/10.1049/el:20064330
  2. S. Chikkerur, A. N. Cartwright, and V. Govindaraju, "Fingerprint enhancement using STFT analysis," Pattern Recognition, vol. 40, no.1, pp. 198-211, 2007 https://doi.org/10.1016/j.patcog.2006.05.036
  3. L. Fausett, Fundamentals of Neural Networks Architecture, Algorithm and Application, Prentice Hall, 1994
  4. R. C. Gonzalez and R. E. woods, Digital Image Processing, 2nd Edition, Prentice Hall, 2002
  5. L. Hong, Y. Wang, and A. K. Jain, "Fingerprint image enhancement: Algorithm and performance evaluation," Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 777-789, 1998
  6. M. K. Hu, "Visual pattern recognition by moment invariants," IRE Trans. on Information Theory, vol. 8, pp.179-187, 1962
  7. A. K. Jain, S. Prabhakar, L. Hong, and S. Pankanti, "Filterbank-based fingerprint matching," IEEE Trans. on Image Processing, vol. 9, pp. 846-859, 2000 https://doi.org/10.1109/83.841531
  8. X. Jang and W. Y. Yau, "Fingerprint minutiae matching based on the local and global structures," Proc. of Int. Conf. on Pattern Recognition, vol. 2, pp. 1024-1045, 2000
  9. A. T. B. Jin, D. N. C. Ling, and O. T. Song, "An efficient fingerprint verification system using integrated wavelet and Fourier-Mellin invariant transform," Image and Vision Computing, vol.22, no. 6, pp. 503-513, 2004 https://doi.org/10.1016/j.imavis.2003.12.002
  10. J. Liu, Z. Huang, and K. Chan, "Direct minutiae extraction from gray-level fingerprint image by relationship examination," Proc. of Int. Conf. on Image Processing, vol. 2, pp. 427-430, 2000
  11. M. Liu, X. D. Jiang, and A. Kot, "Fingerprint reference-point detection," EURASIP Journal on Applied Signal Processing, vol. 2005, no. 4, pp. 498-509, 2005 https://doi.org/10.1155/ASP.2005.498
  12. M. Liu, X. D. Jiang, and A. Kot, "Fingerprint retrieval by complex filter Responses," Proc. of Int. Conf. on Pattern Recognition, HongKong, pp. 1042-1045, 2006
  13. D. Maio, D. Maltoni, R. Cappelli, J. L. Wayman, and A. K. Jain, "FVC2002: Second fingerprint verification competition," Proc. 16th Int'l Conf. Pattern Recognition, vol. 3, pp. 811-814, 2002
  14. D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, Springer, Berlin, 2003
  15. K. Nilsson and J. Bigun, "Localization of corresponding points in fingerprints by complex filtering," Pattern Recognition Letters, vol. 24, pp. 2135-2144, 2003 https://doi.org/10.1016/S0167-8655(03)00083-7
  16. L. F. Sha, F. Zhao, and X. O. Tang, "Improved fingercode for filterbank-based fingerprint matching," Proc. of Int. Conf. on Image Processing, vol. 2, pp. 895-898, 2003
  17. M. Tico, P. Kuosmanen, and J. Saarinen, "Wavelet domain features for fingerprint recognition," Electronics Letters, vol. 37, no.1, pp. 21-22, 2001
  18. J. C. Yang and D. S. Park, "A fingerprint verification algorithm using tessellated invariant moment features," Neurocomputing, vol. 71, no. 10-12, pp. 1939-1946, 2008 https://doi.org/10.1016/j.neucom.2007.12.034
  19. J. C. Yang, S. Yoon, and D. S. Park, "Applying learning vector quantization neural network for fingerprint matching," Lecture Notes in Artificial Intelligence (LNAI 4304), Springer, Berlin, pp. 500-509, 2006
  20. http://bias.csr.unibo.it/fvc2002/