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Robust Iris Segmentation Through Parameterization of the Chan-Vese Algorithm

  • Gugulethu Mabuza-Hocquet
  • Fulufhelo Nelwamondo
  • Tshilidzi Marwala
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 362)

Abstract

The performance of an iris recognition system relies on automated processes from the segmentation stage to the matching stage. Each stage has traditional algorithms used successfully over the years. The drawback is that these algorithms assume that the pupil-iris boundaries are perfect circles sharing the same center, hence only use circle fitting methods for segmentation. The side effect posed by the traditional rubber sheet model used for normalization is; one cannot work backwards to place the discriminative features on the original image. This paper proposes a different approach to each stage using algorithms different from the traditional ones to address the above issues. Bresenham’s circle algorithm to locate and compute pupil-iris boundaries. Chan-Vese algorithm with pre-defined initial contour and curve evolution parameters for accurate segmentation. Preprocessing techniques to enhance and detect iris features for extraction. Labeling features of strongest pixel connectivity and using Harris algorithm for feature extraction and matching.

Keywords

Iris segmentation Chan-Vese algorithm Sobel edge detector Harris corner detector Feature matching 

Notes

Acknowledgments

To UBIRIS for their iris database. The other iris images used were collected from willing participants within the Council for Scientific and Industrial Research (CSIR), South Africa.

References

  1. 1.
    Daugman, J.: How Iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)CrossRefGoogle Scholar
  2. 2.
    Poursaberi, A., Araabi, B.N.: Iris recognition for partial occluded images: methodology and sensitivity analysis. EURASIP J. Adv. Signal, 12 (2006), Article ID: 36751Google Scholar
  3. 3.
    Wildes, R.P.: Iris recognition: an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)CrossRefGoogle Scholar
  4. 4.
    Vatsa, M., Singh, R., Noore, A.: Improving Iris recognition performance using segmentation, quality enhancement, match score fusion and indexing. IEEE Trans. Syst. Man Cybernet. Part B: Cybernet 38(4), 1021–1035 (2008)CrossRefGoogle Scholar
  5. 5.
    Liu, X., Bowyer, K.W., Flynn, P.J.: Experiments with an improved Iris segmentation algorithm. In: Proceedings of the 4th IEEE Workshop Automatic Identification Advanced Technologies, pp. 118–123 (2005)Google Scholar
  6. 6.
    Crandall, R.: Image Segmentation using the Chan-Vese algorithm. ECE 532 Project. Fall. (2009)Google Scholar
  7. 7.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1, 321–331 (1988)Google Scholar
  8. 8.
    Chan, T., Vese, L.: Active contour models without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)CrossRefzbMATHGoogle Scholar
  9. 9.
    Yahya, A.E., Nordin, M.J.: A new technique for Iris localization in iris recognition system. J. Comput. Sci. 6(5), 527–532 (2010)CrossRefGoogle Scholar
  10. 10.
    Yanto, G., Jaward, M.H., Kamrani, N.: Bayesian Chan-Vese segmentation for iris segmentation. In: IEEE Transactions on Visual Communication and Image Processing, pp. 1–6 (2013)Google Scholar
  11. 11.
    Maini, R., Aggarwal, H.: A comprehensive review of image enhancement techniques. J. Comput. 2(3), 8–12 (2010)Google Scholar
  12. 12.
    Vincent O.R., Folorunso, O.: A descriptive algorithm for Sobel image edge detection. In: Proceedings of Informing Science and IT Education Conference, pp. 97–107 (2009)Google Scholar
  13. 13.
    Bresenham, J.: A linear algorithm for incremental display of circular arcs. Commun. ACM 20(2), 100–106 (1977)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Gugulethu Mabuza-Hocquet
    • 1
    • 2
  • Fulufhelo Nelwamondo
    • 1
    • 2
  • Tshilidzi Marwala
    • 2
  1. 1.Modelling and Digital ScienceCouncil for Scientific and Industrial ResearchPretoriaSouth Africa
  2. 2.Engineering and the Built EnvironmentUniversity of JohannesburgJohannesburgSouth Africa

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