Watershed Based Iris SEgmentation

  • Maria Frucci
  • Michele Nappi
  • Daniel Riccio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7914)

Abstract

Recently, the research interest on biometric systems and applications has significantly grown up, aiming to bring the benefits of biometrics to the broader range of users. As signal processing and feature extraction play a very important role for biometric applications, they can be thought as a particular subset of pattern recognition techniques. Most of iris biometric systems have been designed for security applications and work on near-infrared (NIR) images. NIR images are not affected by illumination changes in visible light making systems working both in darker and lighter conditions. The reverse of the medal is a very short distance allowed between the acquisition camera and the user, further than a strictly controlled pose of the eye. For those reasons, the viability of NIR image based systems in commercial applications is quite limited. Several efforts have been devoted to designing new iris biometric approaches on color images acquired in visible wavelength light (VW). However, illumination changes significantly affect the iris pattern as well as the periocular region making both segmentation and feature extraction harder than in NIR. In the specific case of iris biometrics, segmentation represents a crucial aspect, as it must be fast as well as accurate. To this aim, a new watershed based approach for iris segmentation in color images is presented in this paper. The watershed transform is exploited to binarize an image of the eye, while circle fitting together with a ranking approach is applied to first approximate the iris boundary with a circle. The experimental results demonstrate this approach to be effective with respect to location accuracy.

Keywords

iris segmentation watershed circle fitting 

References

  1. 1.
    Daugman, J.G.: New methods in iris recognition. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 37(5), 1167–1175 (2007)CrossRefGoogle Scholar
  2. 2.
    Daugman, J.G.: How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 21–30 (2004)CrossRefGoogle Scholar
  3. 3.
    Daugman, J.G.: How Iris Recognition Works. IEEE Trans. on CSVT 14(1), 21–30 (2004)Google Scholar
  4. 4.
    De Marsico, M., Nappi, M., Riccio, D.: IS_IS: Iris Segmentation for Identification Systems. In: Proc. of the International Conference on Pattern Recognition, pp. 2857–2860 (2010)Google Scholar
  5. 5.
    Donida Labati, R., Scotti, F.: Noisy iris segmentation with boundary regularization and reflections removal. Image and Vision Computing 28(2), 270–277 (2010)CrossRefGoogle Scholar
  6. 6.
    Li, P., Liu, X., Xiao, L., Song, Q.: Robust and accurate iris segmentation in very noisy iris images. Image and Vision Computing 28(2), 246–253 (2010)CrossRefGoogle Scholar
  7. 7.
    Meyer, F.: Color image segmentation. In: Proc. of the International Conference on Image Processing and its Applications, pp. 303–306 (1992)Google Scholar
  8. 8.
    Nguyen, V.H., Hakil, K.: A Novel Circle Detection Method for Iris Segmentation. In: Proc. of the Congress on Image and Signal Processing, vol. 3, pp. 620–624 (2008)Google Scholar
  9. 9.
    Phillips, P.J., Scruggs, T., O’Toole, A., Flynn, P.J., Bowyer, K.W., Schott, C., Sharpe, M.: FRVT 2006 and ICE 2006 Large-Scale (2006)Google Scholar
  10. 10.
    Proenca, H., Alexandre, L.A.: UBIRIS: A noisy iris image database. In: Proc. of the International Conference on Image Analysis and Processing, pp. 970–977 (2005)Google Scholar
  11. 11.
    Puhan, N.B., Sudha, N.: A novel iris database indexing method using the iris color. In: Proc. of the 3rd IEEE Conf. on Industrial Electronics and Applications, pp. 1886–1891 (2008)Google Scholar
  12. 12.
    Roerdink, J.B.T.M., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundamenta Informaticae 41, 187–228 (2001)MathSciNetGoogle Scholar
  13. 13.
    Special Issue on the Segmentation of Visible Wavelength Iris Images Captured At-a-distance and On-the-move. Image and Vision Computing 28 (2010)Google Scholar
  14. 14.
    Special Issue on the Recognition of Visible Wavelength Iris Images Captured At-a-distance and On-the-move. Pattern Recognition Letters 33 (2012)Google Scholar
  15. 15.
    Tan, T., He, Z., Sun, Z.: Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image and Vision Computing 28(2), 223–230 (2010)CrossRefGoogle Scholar
  16. 16.
    Taubin, G.: Estimation of Planar Curves, Surfaces And Nonplanar Space Curves Defined By Implicit Equations, With Applications To Edge And Range Image Segmentation. IEEE Trans. on PAMI 13, 1115–1138 (1991)CrossRefGoogle Scholar
  17. 17.
    Wildes, R.: Iris recognition: an emerging biometric technology. Proceedings of the IEEE 85(9), 1348–1363 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Maria Frucci
    • 1
  • Michele Nappi
    • 2
  • Daniel Riccio
    • 3
  1. 1.Istituto di Cibernetica “E. Caianiello”, CNRNapoliItaly
  2. 2.Università degli Studi di SalernoFiscianoItaly
  3. 3.Università degli Studi di Napoli Federico IINapoliItaly

Personalised recommendations