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An Efficient Fingerprint Image Thinning Algorithm

Published: 20 February 2013
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Abstract

Most fingerprint recognition applications rely heavily on efficient and fast image enhancement algorithms. Image thinning is a very important stage of image enhancement. A good thinning algorithm preserves the structure of the original fingerprint image, reduces the amount of data needed to process and helps improve the feature extraction accuracy and efficiency. In this paper we describe and compare some of the most used fingerprint thinning algorithms. Results show that faster algorithms have difficulty preserving connectivity. Zhang and Suen’s algorithm gives the least processing time, while Guo and Hall’s algorithm produces the best skeleton quality. A modified Zhang and Suen’s algorithm is proposed, that is efficient and fast, and better preserves structure and connectivity.

Published in American Journal of Software Engineering and Applications (Volume 2, Issue 1)
DOI 10.11648/j.ajsea.20130201.11
Page(s) 1-6
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2013. Published by Science Publishing Group

Keywords

Image Thinning; Fingerprint Recognition; Minutiae; Image Enhancement

References
[1] Davide Maltoni, Dario Maio, Handbook of Fingerprint Recognition, Springer, 2009.
[2] Z. Guo and R. Hall, "Parallel thinning with two-subiteration algorithms,"Communications of the ACM, vol. 32, pp. 359–373, Mar 1989.
[3] R. Gupta and R. Kaur, "Skeletonization algorithm for numerical patterns", International Jornal of Signal Processing, Image Processing andPattern Recognition, vol. 01, pp. 63–72, Dec 2008.
[4] T. Zhang and C. Suen, "A fast parallel algorithm for thinning digital patterns," Communications of the ACM, vol. 27, pp. 236–239, Mar 1984.
[5] W. Abdulla, A. Saleh, and A. Morad, "A preprocessing algorithm for hand-written character recognition",Pattern Recognition Letters 7, pp. 13–18, 1988.
[6] R. Hall, "Fast parallel thinning algorithms: Parallel speed and connectivity preservation," Communications of the ACM, vol. 32, pp. 124–129, Jan 1989.
[7] B. Jang and T. Chin, "One-pass parallel thinning: Analysis, properties, and quantitative evaluation," IEEE Transactions on Pattern Analysis andMechine Intelligence, pp. 1129–1140, 1992.
[8] G. Raju and Y. Xu, "Study of parallel thinning algorithms," IEEE International Conference on Systems, Man, and Cybernetics, vol. 01,pp. 661–666, Dec 1991.
[9] S. Prabhakar, A. K. Jain and S. Pankanti, "Learning fingerprint minutiae location and type", Pattern Recognition, 36(8): 1847–1857, 2003.
[10] Kalyani Mali, Samayita Bhattacharya, Fingerprint Recognition Using Global and Local Structures, International Journal on Computer Science and Engineering, Vol. 3 No. 1 Jan 2011.Ratha, Nalini; Bolle, Ruud, "Automatic Fingerprint Recognition Systems", Springer, XVII, 458 p. 135.
[11] James L. Wayman (Editor), Anil K. Jain, "Biometric Systems", Springer, 2004.
[12] Bir Bhanu,Xuejun Tan, "Computational Algorithms for Fingerprint Recognition", Springer, 2004.
Cite This Article
  • APA Style

    Davit Kocharyan. (2013). An Efficient Fingerprint Image Thinning Algorithm. American Journal of Software Engineering and Applications, 2(1), 1-6. https://doi.org/10.11648/j.ajsea.20130201.11

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    ACS Style

    Davit Kocharyan. An Efficient Fingerprint Image Thinning Algorithm. Am. J. Softw. Eng. Appl. 2013, 2(1), 1-6. doi: 10.11648/j.ajsea.20130201.11

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    AMA Style

    Davit Kocharyan. An Efficient Fingerprint Image Thinning Algorithm. Am J Softw Eng Appl. 2013;2(1):1-6. doi: 10.11648/j.ajsea.20130201.11

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  • @article{10.11648/j.ajsea.20130201.11,
      author = {Davit Kocharyan},
      title = {An Efficient Fingerprint Image Thinning Algorithm},
      journal = {American Journal of Software Engineering and Applications},
      volume = {2},
      number = {1},
      pages = {1-6},
      doi = {10.11648/j.ajsea.20130201.11},
      url = {https://doi.org/10.11648/j.ajsea.20130201.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20130201.11},
      abstract = {Most fingerprint recognition applications rely heavily on efficient and fast image enhancement algorithms. Image thinning is a very important stage of image enhancement. A good thinning algorithm preserves the structure of the original fingerprint image, reduces the amount of data needed to process and helps improve the feature extraction accuracy and efficiency. In this paper we describe and compare some of the most used fingerprint thinning algorithms. Results show that faster algorithms have difficulty preserving connectivity. Zhang and Suen’s algorithm gives the least processing time, while Guo and Hall’s algorithm produces the best skeleton quality. A modified Zhang and Suen’s algorithm is proposed, that is efficient and fast, and better preserves structure and connectivity.},
     year = {2013}
    }
    

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    T1  - An Efficient Fingerprint Image Thinning Algorithm
    AU  - Davit Kocharyan
    Y1  - 2013/02/20
    PY  - 2013
    N1  - https://doi.org/10.11648/j.ajsea.20130201.11
    DO  - 10.11648/j.ajsea.20130201.11
    T2  - American Journal of Software Engineering and Applications
    JF  - American Journal of Software Engineering and Applications
    JO  - American Journal of Software Engineering and Applications
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    EP  - 6
    PB  - Science Publishing Group
    SN  - 2327-249X
    UR  - https://doi.org/10.11648/j.ajsea.20130201.11
    AB  - Most fingerprint recognition applications rely heavily on efficient and fast image enhancement algorithms. Image thinning is a very important stage of image enhancement. A good thinning algorithm preserves the structure of the original fingerprint image, reduces the amount of data needed to process and helps improve the feature extraction accuracy and efficiency. In this paper we describe and compare some of the most used fingerprint thinning algorithms. Results show that faster algorithms have difficulty preserving connectivity. Zhang and Suen’s algorithm gives the least processing time, while Guo and Hall’s algorithm produces the best skeleton quality. A modified Zhang and Suen’s algorithm is proposed, that is efficient and fast, and better preserves structure and connectivity.
    VL  - 2
    IS  - 1
    ER  - 

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Author Information
  • Digital Signal and Image Processing Laboratory

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