Using the Watershed Transform for Iris Detection

  • Maria Frucci
  • Michele Nappi
  • Daniel Riccio
  • Gabriella Sanniti di Baja
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)


Iris biometric systems are of interest for security applications. In this respect, iris segmentation has a key role, as it must be fast and accurate. In this paper, we present a new watershed based approach for iris segmentation in color images. The watershed transform is used in two distinct phases of iris segmentation: it is first used to obtain a preliminary segmentation, which constitutes the input to a circle fitting procedure; then, it is used together with the portion of the input image resulting after circle fitting to identify more precisely the pixels actually belonging to the iris. The experimental results show that the suggested approach is effective with respect to both location accuracy and computational complexity.


Biometrics iris detection watershed transformation circle fitting 


  1. 1.
    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
  2. 2.
    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
  3. 3.
    Wildes, R.: Iris recognition: an emerging biometric technology. Proceedings of the IEEE 85(9), 1348–1363 (1997)CrossRefGoogle Scholar
  4. 4.
    Daugman, J.G.: How iris recognition works. IEEE Trans. Circuits and Systems for Video Technology 14(1), 21–30 (2004)CrossRefGoogle Scholar
  5. 5.
    Puhan, N.B., Sudha, N.: A novel iris database indexing method using the iris color. In: Proc. of the IEEE Conference on Industrial Electronics and Applications, pp. 1886–1891 (2008)Google Scholar
  6. 6.
    Proença, H., Alexandre, L.A.: UBIRIS: A noisy iris image database. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 970–977. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Beucher, S., Lantuejoul, C.: Use of watersheds in contour detection, in. In: Proc. Int. Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation, France (1979)Google Scholar
  8. 8.
    Roerdink, J.B.T.M., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundamenta Informaticae 41, 187–228 (2001)MathSciNetGoogle Scholar
  9. 9.
    Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 111–122 (1981)CrossRefzbMATHGoogle Scholar
  10. 10.
    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
  11. 11.
    Masek, L., Kovesi, P.: MATLAB Source Code for a Biometric Identification System Based on Iris Patterns. The School of Computer Science and Software Engineering (2003)Google Scholar
  12. 12.
    De Marsico, M., Nappi, M., Riccio, D.: IS_IS: Iris Segmentation for Identification Systems. In: 20th International Conference on Pattern Recognition, pp. 2857–2860 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Maria Frucci
    • 1
  • Michele Nappi
    • 2
  • Daniel Riccio
    • 3
  • Gabriella Sanniti di Baja
    • 1
  1. 1.Istituto di Cibernetica “E. Caianiello”Napoli
  2. 2.Università degli Studi di SalernoFiscianoItaly
  3. 3.Università degli Studi di Napoli Federico IINapoli

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