Eyelid Localization in Iris Images Captured in Less Constrained Environment

  • Xiaomin Liu
  • Peihua Li
  • Qi Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

Eyelid localization plays an important role in an accurate iris recognition system. In less constrained environment where the subjects are less cooperative, the problem becomes very difficult due to interference of eyelashes, eyebrows, glasses, hair and diverse variation of eye size and position. To determine upper eyelid boundary accurately, the paper proposes an integro-differential parabolic arc operator combined with a RANSAC-like algorithm. The integro-differential operator works as a parabolic arc edge detector. During search process of the operator, the potential candidate parabolas should near at least certain percentage of edgels of upper eyelid boundary, detected by 1D edge detector. The RANSAC-like algorithm functions as a constraint that not only makes eyelid localization more accurate, but also enables it more efficient by excluding invalid candidates for further processing. Lower eyelid localization is much simpler due to very less interference involved, and a method is presented that exploits 1D edgels detection and an RANSAC algorithm for parabolic fitting. Experiments are made on UBIRIS.v2 where images were captured at-a-distance and on-the-move. The comparison shows that the proposed algorithm is quite effective in localizing eyelids in heterogeneous images.

Keywords

Eyelid localization Integro-differential parabolic arc operator RANSAC 1D Signal 

References

  1. 1.
    UBIRIS.v2 database (2008), http://iris.di.ubi.pt/ubiris2.html
  2. 2.
    Blake, A., Isard, M.: Active Contours. Springer, London (1998)Google Scholar
  3. 3.
    Bowyer, K.W., Hollingsworth, K., Flynn, P.J.: Image understanding for iris biometrics: a survey. Comp. Vis. Image Understanding 110(2), 281–307 (2008)Google Scholar
  4. 4.
    Cui, J., Wang, Y., Tan, T., Sun, Z.: A fast and robust iris localization method based on texture segmentation. In: Proc. of SPIE, pp. 401–408 (2004)Google Scholar
  5. 5.
    Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1160 (1993)Google Scholar
  6. 6.
    Daugman, J.G.: The importance of being random: Statistical principles of iris recognition. Pattern Recognition 36(2), 279–291 (2003)Google Scholar
  7. 7.
    Daugman, J.G.: New methods in iris recognition. IEEE Trans. Systems, Man, Cybernetics B 37(5), 1167–1175 (2007)Google Scholar
  8. 8.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24, 381–395 (1981)Google Scholar
  9. 9.
    Jang, Y.K., Kang, B.J., Park, K.R.: A study on eyelid localization considering image focus for iris recognition. Pattern Recognition Letters 29(1), 1698–1704 (2008)Google Scholar
  10. 10.
    Liu, X., Bowyer, K.W., Flynn, P.J.: Experiments with an improved iris segmentation algorithm. In: IEEE Workshop on Automatic Identification Advanced Technologies (AutoID), pp. 118–123 (2005)Google Scholar
  11. 11.
    Masek, L.: Recognition of human iris patterns for biometric identification. Bachelors Thesis (2003)Google Scholar
  12. 12.
    Wildes, R.P.: Iris recognition:an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Xiaomin Liu
    • 1
  • Peihua Li
    • 1
  • Qi Song
    • 1
  1. 1.College of Computer Science and TechnologyHeilongjiang UniversityHarbin, Hei Long Jiang ProvinceChina

Personalised recommendations