Low Cost Eyelid Occlusion Removal to Enhance Iris Recognition

  • Beeren Sahu
  • Soubhagya S. Barpanda
  • Sambit Bakshi
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 43)


The Iris recognition system is claimed to perform with very high accuracy in given constrained acquisition scenarios. It is also observed that partial occlusion due to the presence of the eyelid can hinder the functioning of the system. State-of-the-art algorithms consider that the inner and outer iris boundaries are circular and thus these algorithms do not take into account the occlusion posed by the eyelids. In this paper, a novel low-cost approach for detecting and removing eyelids from annular iris is proposed. The proposed scheme employs edge detector to identify strong edges, and subsequently chooses only horizontal edges. 2-means clustering technique clusters the upper and lower eyelid edges through maximizing class separation. Once two classes of edges are formed, one indicating edges contributing to upper eyelid, another indicating lower eyelid, two quadratic curves are fitted on each set of edge points. The area above the quadratic curve indicating upper eyelid, and below as lower eyelid can be suppressed. Only non-occluded iris data can be fetched to the further biometric system. This proposed localization method is tested on publicly available BATH and CASIAv3 iris databases, and has been found to yield very low mislocalization rate.


Iris recognition Personal identification Eyelid detection 


  1. 1.
  2. 2.
    Chinese academy of sciences’ institute of automation (casia) iris image database v3.0:
  3. 3.
    Cui, J., Wang, Y., Tan, T., Ma, L., Sun, Z.: A fast and robust iris localization method based on texture segmentation. In: Defense and Security, pp. 401–408. International Society for Optics and Photonics (2004)Google Scholar
  4. 4.
    Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)CrossRefGoogle Scholar
  5. 5.
    Jain, A.K., Ross, P.F., Arun, A.: Handbook of Biometrics. Springer (2007)Google Scholar
  6. 6.
    Ling, L.L., de Brito, D.F.: Fast and efficient iris image segmentation. J. Med. Biol. Eng. 30(6), 381–391 (2010)CrossRefGoogle Scholar
  7. 7.
    Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal identification based on iris texture analysis. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1519–1533 (2003)CrossRefGoogle Scholar
  8. 8.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press (1967)Google Scholar
  9. 9.
    Mahlouji, M., Noruzi, A.: Human iris segmentation for iris recognition in unconstrained environments. IJCSI Int. J. Comput. Sci. Issues 9(3), 149–155 (2012)Google Scholar
  10. 10.
    Masek, L.: Recognition of human iris patterns for biometric identification. Ph.D. thesis, Masters thesis, University of Western Australia (2003)Google Scholar
  11. 11.
    Radman, A., Zainal, N., Ismail, M.: Efficient iris segmentation based on eyelid detection. J. Eng. Sci. Technol. 8(4), 399–405 (2013)Google Scholar
  12. 12.
    Wildes, R.P.: Iris recognition: an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)CrossRefGoogle Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Beeren Sahu
    • 1
  • Soubhagya S. Barpanda
    • 1
  • Sambit Bakshi
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of Technology RourkelaRourkelaIndia

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