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Learning Features for Fingerprint Classification

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Audio- and Video-Based Biometric Person Authentication (AVBPA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2688))

Abstract

In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses visually meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from NIST-4, the correct rates for 4 and 5-class classification are 93.2% and 91.2% respectively, which compare favorably and have advantages over the best results published to date.

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References

  1. J.R. Koza, Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press, 1994.

    Google Scholar 

  2. R. Poli, Genetic programming for feature detection and image segmentation, Evolutionary Computation, T.C. Forgarty Ed., pp. 110–125, 1996.

    Google Scholar 

  3. S.A. Stanhope and J.M. Daida, Genetic programming for automatic target classification and recognition in synthetic aperture radar imagery, Proc. Evolutionary Programming VII, pp. 735–744, 1998.

    Google Scholar 

  4. D. Howard, S.C. Roberts, and R. Brankin, Target detection in SAR imagery by genetic programming, Advances in Eng. Software, 30(5), pp. 303–311, May 1999.

    Article  Google Scholar 

  5. S.C. Roberts and D. Howard, Evolution of vehicle detectors for infrared line scan imagery, Proc. Evolutionary Image Analysis, Signal Processing and Telecommunications, pp. 110–125, 1999.

    Google Scholar 

  6. A.M. Bazen and S.H. Gerez, Systematic methods for the computation of the directional fields and singular points of fingerprints, IEEE Trans. on PAMI, vol. 24, no. 7, pp. 905–919, July 2002.

    Google Scholar 

  7. C.I. Watson and C.L. Wilson, NIST special database 4, fingerprint database, U.S. National Institute of Standards and Technology, 1992.

    Google Scholar 

  8. K. Karu and A.K. Jain, Fingerprint classification, Pattern Recognition, 29(3), pp. 389–404, 1996.

    Article  Google Scholar 

  9. R. Cappelli, A. Lumini, D. Maio and D. Maltoni, Fingerprint classification by directional image partitioning, IEEE Trans. PAMI, vol. 21, no. 5, pp. 402–421, 1999.

    Google Scholar 

  10. G.T. Candela, P.J. Grother, C.I. Watson, R.A. Wilkinson and C.L. Wilson, PCASYS — a pattern-level classification automation system for fingerprints, Technical Report NISTIR 5647, NIST, Apr. 1995.

    Google Scholar 

  11. U. Halici and G. Ongun, Fingerprint classification through self-organizing feature maps modified to treat uncertainties, Proc. IEEE, vol. 84, no. 10, pp. 1497–1512, Oct. 1996.

    Google Scholar 

  12. A.K. Jain, S. Prabhakar and L. Hong, A multichannel approach to fingerprint classification, IEEE Trans. on PAMI, vol. 21, no. 4, pp. 348–359, Apr. 1999.

    Google Scholar 

  13. A. Senior, A combination fingerprint classifier, IEEE Trans. on PAMI, 23(10), pp. 1165–1174, 2001.

    Google Scholar 

  14. Y. Yao, G.L. Marcialis, M. Pontil, P. Frasconi and F. Roli, Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines, Pattern Recognition, vol. 36, no. 2, pp. 397–406, Feb. 2003.

    Article  Google Scholar 

  15. A.K. Jain and S. Minut, Hierarchical kernel fitting for fingerprint classification and alignment, Proc. ICPR, vol. 2, pp. 469–473, 2002.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Center for Research in Intelligent Systems, University of California, 92521, Riverside, CA, USA

    Xuejun Tan, Bir Bhanu & Yingqiang Lin

Authors
  1. Xuejun Tan
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  2. Bir Bhanu
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  3. Yingqiang Lin
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Editor information

Editors and Affiliations

  1. Center for Vision, Speech and Signal Proc., University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Josef Kittler

  2. Department of Electronics and Computer Science, University of Southampton, SO17 1BJ, Southampton, UK

    Mark S. Nixon

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© 2003 Springer-Verlag Berlin Heidelberg

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Tan, X., Bhanu, B., Lin, Y. (2003). Learning Features for Fingerprint Classification. In: Kittler, J., Nixon, M.S. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2003. Lecture Notes in Computer Science, vol 2688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44887-X_38

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  • DOI: https://doi.org/10.1007/3-540-44887-X_38

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40302-9

  • Online ISBN: 978-3-540-44887-7

  • eBook Packages: Springer Book Archive

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