Ophthalmic Disorder Menagerie and Iris Recognition

  • Ishan Nigam
  • Mayank Vatsa
  • Richa Singh
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


Popularity of iris biometrics has led to large scale deployment of large-scale authentication systems such as India’s Aadhar project and UAE border control system. For such projects, maintaining high image quality standards during enrollment as well as recognition becomes important. It is also important to handle diversity in iris patterns so that error rates are reduced and all citizens are enrolled in the system. While traditional covariates such as illumination and pose variations are well explored, challenges due to ophthalmic disorders or medical conditions are overlooked. This chapter focuses on the “Ophthalmic Disorder Menagerie” and its effect on iris recognition. The experimental observations suggest that such conditions should also be considered for large scale iris recognition systems.


Cataract Surgery Iris Image Motion Blur Biometric System Iris Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was partially supported by a grant from the Department of Electronics and Information Technology, Government of India. The authors acknowledge Sunpreet Singh Arora in collecting a part of IIITD iris database used in this research.


  1. 1.
    American Academy of Ophthalmology. EyeSmart, in (2014)Google Scholar
  2. 2.
    S.S. Arora et al., Iris recognition under alcohol influence: a preliminary study, in 5th IAPR International Conference on Biometrics (ICB). IEEE (2012), pp. 336–341Google Scholar
  3. 3.
    T.M. Aslam, S.Z. Tan, B. Dhillon, Iris recognition in the presence of ocular disease. J. R. Soc. Interface 6(34), 489–493 (2009)CrossRefGoogle Scholar
  4. 4.
    Atlas of Ophthalmology, Online Multimedia Databsae, in (2015)Google Scholar
  5. 5.
    S. Bharadwaj, M. Vatsa, R. Singh, Biometric quality: a review of fingerprint, iris, and face. EURASIP J. Image Video Process. 2014(1), 1–28 (2014)CrossRefGoogle Scholar
  6. 6.
    W.K. Bickel et al., Buprenorphine: dose-related blockade of opioid challenge effects in opioid dependent humans. J. Pharmacol. Exp. Ther. 247(1), 47–53 (1988)MathSciNetGoogle Scholar
  7. 7.
    K.W. Bowyer, E. Ortiz, A. Sgroi, Trial somaliland voting register de-duplication using iris recognition, in 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol. 2. IEEE (2015), pp. 1–8Google Scholar
  8. 8.
    T.F. Chan, L. Vese, Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)Google Scholar
  9. 9.
    Cogan Ophthalmic Pathology Collection, in (2008)Google Scholar
  10. 10.
    J. Daugman, How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)CrossRefGoogle Scholar
  11. 11.
    J. Daugman, High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)Google Scholar
  12. 12.
    L. Dhir et al., Effect of cataract surgery and pupil dilation on iris pattern recognition for personal authentication. Eye 24(6), 1006–1010 (2010)CrossRefGoogle Scholar
  13. 13.
    N.D. Kalka et al., Estimating and fusing quality factors for iris biometric images. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 40(3), 509–524 (2010)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Y. Lachkar, W. Bouassida, Drug-induced acute angle closure glaucoma. Curr. Opin. Ophthalmol. 18(2), 129–133 (2007)CrossRefGoogle Scholar
  15. 15.
    R.R. Murphy, Adaptive rule of combination for observations over time, in International Conference on Multisensor Fusion and Integration for Intelligent Systems. IEEE (1996), pp. 125–131Google Scholar
  16. 16.
    R.R. Murphy, Dempster-Shafer theory for sensor fusion in autonomous mobile robots. IEEE Trans. Robot. Autom. 14(2), 197–206 (1998)CrossRefGoogle Scholar
  17. 17.
    G. Murthy et al., Current status of cataract blindness and vision 2020: the right to sight initiative in India. Indian J. Ophthalmol. 56(6), 489 (2008)Google Scholar
  18. 18.
    Neurotechnology VeriEye Software Development KitGoogle Scholar
  19. 19.
    Prevalence of Adult Vision Impairment and Age-Related Eye Diseases in America, in (2010)Google Scholar
  20. 20.
    I. Rennie, Dont it make my blue eyes brown: heterochromia and other abnormalities of the iris. Eye 26(1), 29–50 (2012)CrossRefGoogle Scholar
  21. 21.
    M. Rosenfield, N. Logan, K.H. Edwards, Optometry: Science, Techniques and Clinical Management (Elsevier Health Sciences, 2009)Google Scholar
  22. 22.
    O. Seyeddain et al., Reliability of automatic biometric iris recognition after phacoemulsification or drug-induced pupil dilation. Eur. J. Ophthalmol. 24(1), 58–62 (2014)CrossRefGoogle Scholar
  23. 23.
    G. Shafer, A Mathematical Theory of Evidence, (Princeton University Press, 1976)Google Scholar
  24. 24.
    C.-W. Tan, A. Kumar, Towards online iris and periocular recognition under relaxed imaging constraints. IEEE Trans. Image Process. 22(10), 3751–3765 (2013)MathSciNetCrossRefGoogle Scholar
  25. 25.
    The United States of America Census, in (2010)Google Scholar
  26. 26.
    M. Trokielewicz, A. Czajka, P. Maciejewicz, Cataract influence on iris recognition performance, in Symposium on Photonics Applications in Astronomy, Communications, Industry and High-Energy Physics Experiments. International Society for Optics and Photonics (2014), pp. 929020–929020Google Scholar
  27. 27.
    A. Tsai, A. Yezzi Jr., A.S. Willsky, Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. Image Process. 10(8), 1169–1186 (2001)CrossRefzbMATHGoogle Scholar
  28. 28.
    M. Vatsa, R. Singh, A. Noore, Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 38(4), 1021–1035 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  1. 1.IIITDelhiIndia

Personalised recommendations