A Pixel Based Segmentation Scheme for Fingerprint Images

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 340)

Abstract

Fingerprint segmentation, an important step in Automatic Fingerprint Identification System (AFIS) helps reduce the time of subsequent processing. It aims at separating the foreground region from the background. This paper presents a pixel-wise segmentation scheme based on mathematical moment which provides a proper discrimination of the pixel intensities. A global threshold value is estimated from a set of local blocks with higher standard deviation. The relative local threshold values are derived subsequently to decide whether a pixel belongs to the foreground or background. Finally, morphological filtering is employed as post-processing step to identify the entire foreground region. The proposed method has been implemented and tested on a set of fingerprint images and the experimental results visually establish the effectiveness of the method. Besides, a comparison with the existing methods is presented to verify the accuracy of the proposed algorithm.

Keywords

Fingerprint segmentation Pixel-wise segmentation Mathematical moment Morphological filter 

References

  1. 1.
    Klein, S., Bazen, A., Veldhuis, R.: Fingerprint image segmentation based on Hidden Markov Models. In: Proceedings of 13th Annual Workshop on Circuits, Systems, and Signal Processing, pp. 310–318 (2002)Google Scholar
  2. 2.
    Ong, T S., Andrew, T.B.J., David, N.C.L., Sek, Y.W.: Fingerprint images segmentation using two stages coarse to fine discrimination technique. In: Proceedings of 16th Australian Joint Conference on Artificial Intelligence, Perth Australia, LNAI. 2903, pp. 624–633 (2003)Google Scholar
  3. 3.
    Yin, J.P., Zhu, E., Yang, X.J., Zhang, G.M., Hu, C.F.: Two steps for fingerprint segmentation. J. Image Vision Comput. 25, 1391–1403 (2007)CrossRefGoogle Scholar
  4. 4.
    Zhu, E., Yin, J., Hu, C., Zhang, G.: A systematic method for fingerprint ridge orientation estimation and image segmentation. Patt. Recogn. 39, 1452–4172 (2006)CrossRefMATHGoogle Scholar
  5. 5.
    Guo, X., Yin, Y., Shi, Z.: Personalized fingerprint segmentation. In: ICONIP 2009, Part I, LNCS 5836, pp. 798–809 (2009)Google Scholar
  6. 6.
    Wang, L., Dai, M., Geng, G.H.: Fingerprint image segmentation by energy of Gaussian-Hermite moments. In: Proceedings of Sinobiometrics 2004, LNCS 3338, pp. 414–423 (2004)Google Scholar
  7. 7.
    Bazen, A.M., Gerez, S.H.: Segmentation of fingerprint images. Workshop on Circuits, Systems, and Signal Processing, pp. 276–280 (2001)Google Scholar
  8. 8.
    Fleyeh, H., Jomma, D., Dougherty, M.: Segmentation of low quality fingerprint images. In: Proceedings of International Conference on Multimeadia Computing and Information Technology (MCIT), pp. 85–88 (2010)Google Scholar
  9. 9.
    Ma, J., Zing, X., Zhang, Y., Sun, S., Huang, H.: Simple effective fingerprint segmentation algorithm for low quality images. In: Proceedings of 3rd IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT), pp. 855–859 (2010)Google Scholar
  10. 10.
    Yin, Y., Wang, Y., Yang, X.: Fingerprint image segmentation based on quadric surface model. In: Proceedings of AVBPA 2005, LNCS 3546, pp. 647–655 (2005)Google Scholar
  11. 11.
    Jomma, D.: Segmentation of low quality fingerprint images. In: Proceedings of ACM (2010)Google Scholar
  12. 12.
    Bernard, S., Boujemaa, N., Vitale, D., Bricot, C.: Fingerprint segmentation using the phase of multiscale gabor wavelets. In: 5th Asian Conference on Computer Vision, Melbourne, Australia (2002)Google Scholar
  13. 13.
    Wu, C., Tulyakov, S., Govindaraju, V.: Robust point-based feature fingerprint segmentation algorithm. In: ICB 2007, LNCS 4642, pp. 1095–1103 (2007)Google Scholar
  14. 14.
    Mehtre, B.M., Chatterjee, B.: Segmentation of fingerprint images- a composite method. Patt. Recogn. 22, 381–385 (1989)CrossRefGoogle Scholar
  15. 15.
    Ren, C., Yin, Y., Ma, J., Yang, G.: A linear hybrid classifier for fingerprint segmentation. In: Proceedings of 4th International Conference on Natural Computation, IEEE, pp. 33–37 (2008)Google Scholar
  16. 16.
    Jomma, D.: Segmentation of low quality fingerprint images. In: Proceedings of ACM (2010)Google Scholar
  17. 17.
    Afsar, F.A., Arif, M., Hussain, M.: An effective approach to fingerprint segmentation using fisher basis. In: 9th International Multitopic Conference, IEEE Explore, pp. 1–6 (2010)Google Scholar
  18. 18.
    Yang, G., Zhou, G.T., Yin, Y., Yang, X.: K-means based fingerprint segmentation with sensor interoperability. EURASIP J. Adv. Signal Process. 2010, 1–12 (2010)CrossRefGoogle Scholar
  19. 19.
    Amayeh, G., Bebis, G., Erol, A., Nicolescu, M.: Hand-based verification and identification using palm-finger segmentation and fusion. Comput. Vis. Image Underst. 113, 477–501 (2009)CrossRefGoogle Scholar
  20. 20.
    Serra, J.: Image Analysis Using Mathematical Morphology. Academic Press, London (1982)MATHGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian School of MinesDhanbadIndia

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