Iris Recognition in the Visible Wavelength

Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


The human iris supports contactless data acquisition and can be imaged covertly. Thus, at least theoretically, the subsequent biometric recognition procedure can be performed without subjects knowledge. The feasibility of this type of recognition has received increasing attention and is of particular interest for forensic and security purposes, such as the pursuit of criminals and terrorists and the search for missing children. Among others, one active research area sought to use visible wavelength (VW) light imagery to acquire data at significantly larger distances than usual and on moving subjects, which is a difficult task because this real-world data is notoriously different from the one used in the near-infrared (NIR) setup. This chapter addresses the feasibility of performing reliable biometric recognition using VW data acquired under dynamic lighting conditions and unconstrained acquisition protocols: with subjects at large distances (between 4 and 8 m) and on-the-move.


Iris Image Visible Wavelength Iris Recognition False Match Human Iris 
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.



The financial support given by FCT-Fundação para a Ciência e Tecnologia and FEDER in the scope of the PTDC/EIA-EIA/103945/2008 NECOVID (Negative Covert Biometric Identification) research project is acknowledged.


  1. 1.
    American National Standards Institute: American national standard for the safe use of lasers and LEDs used in optical fiber transmission systems ANSI Z136-2 (1988)Google Scholar
  2. 2.
    Boddeti, N., Kumar, V.: Extended depth of field iris recognition with correlation filters. In: Proceedings of Computer Vision and Pattern Recognition Workshop on Biometrics, New York, pp. 51–59 (2006)Google Scholar
  3. 3.
    Bowyer, K., Hollingsworth, K., Flynn, P.: Image understanding for iris biometrics: a survey. Comput. Vis. Image Underst. 110, 281–307 (2008)CrossRefGoogle Scholar
  4. 4.
    Boyce, C., Ross, A., Monaco, M., Hornak, L., Li, X.: Multispectral iris analysis: a preliminary study. In: Proceedings of the First IEEE International Conference on Biometrics: Theory, Applications, and Systems, Arlington, pp. 1–8 (2008)Google Scholar
  5. 5.
    Cambier, J.: Iridian large database performance. Tech. rep., Iridian Technologies (2007)Google Scholar
  6. 6.
    Commission International de l’Eclarirage: photobiological safety standards for safety standards for lamps. Report of TC 6-38 CIE 134-3-99 (1999)Google Scholar
  7. 7.
    Daugman, J.: 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
  8. 8.
    Daugman, J.: Biometric decision landscapes. Tech. rep. TR482, University of Cambridge Computer Laboratory (2000)Google Scholar
  9. 9.
    Daugman, J.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)CrossRefGoogle Scholar
  10. 10.
    Daugman, J.: Probing the uniqueness and randomness of iriscodes: results from 200 billion iris pair comparisons. Proc. IEEE 94, 1927–1935 (2006)CrossRefGoogle Scholar
  11. 11.
    Fancourt, C., Bogoni, L., Hanna, K., Guo, Y., Wildes, R., Takahashi, N., Jain, U.: Iris recognition at a distance. In: Proceedings of the 2005 IAPR Conference on Audio and Video Based Biometric Person Authentication, Hilton Rye Town, pp. 1–13 (2005)Google Scholar
  12. 12.
    He, Y., Cui, J., Tan, T., Wang, Y.: Key techniques and methods for imaging iris in focus. In: Proceedings of the IEEE International Conference on Pattern Recognition, Hong Kong, pp. 557–561 (2006)Google Scholar
  13. 13.
    Honeywell International Inc.: A distance iris recognition. United States Patent 0036397 (2007)Google Scholar
  14. 14.
    Imai, F.: Preliminary experiment for spectral reflectance estimation of human iris using a digital camera. Tech. rep., Munsell Color Science Laboratories, Rochester Institute of Technology (2000)Google Scholar
  15. 15.
    International Biometric Group: Independent test of iris recognition technology (2005)Google Scholar
  16. 16.
    Mansfield, T., Kelly, G., Chandler, D., Kane, J.: Biometric product testing final report. National Physical Laboratory, Middlesex (2001)Google Scholar
  17. 17.
    Matey, J.R., Ackerman, D., Bergen, J., Tinker, M.: Iris recognition in less constrained environments. In: Ratha, N.K., Govindaraju, V. (eds.) Advances in Biometrics: Sensors, Algorithms and Systems, pp. 107–131. Springer, New York/London (2007)Google Scholar
  18. 18.
    Miller, P.E., Rawls, A., Pundlik, S., Woodard, D.: Personal identification using periocular skin texture. In: Proceedings of the ACM 25th Symposium on Applied Computing ACM, New York (2010)Google Scholar
  19. 19.
    Narayanswamy, R., Johnson, G., Silveira, P., Wach H.: Extending the imaging volume for biometric iris recognition. Appl. Opt. 44(5), 701–712 (2005)CrossRefGoogle Scholar
  20. 20.
    Nemati, B., Rylander III, H.G., Welch, A.J.: Optical properties of conjunctiva, sclera, and the ciliary body and their consequences for transscleral cyclophotocoagulation. Appl. Opt. 35(19), 3321–3327 (1996)CrossRefGoogle Scholar
  21. 21.
    Park, K., Kim, J.: A real-time focusing algorithm for iris recognition camera. IEEE Trans. Syst. Man Cybern. 35(3), 441–444 (2005)CrossRefGoogle Scholar
  22. 22.
    Park, U., Ross, A., Jain, A.K.: Periocular biometrics in the visible spectrum: a feasibility study. In: Proceedings of the Biometrics Applications and Systems, Washington DC (BTAS), pp. 153–158. IEEE, Piscataway (2009)Google Scholar
  23. 23.
    Ross, A., Crihalmeanu, S., Hornak, L., Schuckers, S.: A centralized web-enabled multimodal biometric database. In: Proceedings of the 2004 Biometric Consortium Conference (BCC), Arlington (2004)Google Scholar
  24. 24.
    Sarna, T., Meredith, P.: The physical and chemical properties of eumelanin. Pigment Cell Res. 19, 572–594 (2010)Google Scholar
  25. 25.
    Smith, K., Pauca, V.P., Ross, A., Torgersen, T., King, M.: Extended evaluation of simulated wavefront coding technology in iris recognition. In: Proceedings of the First IEEE International Conference on Biometrics: Theory, Applications, and Systems, Crystal City, pp. 1–7 (2007)Google Scholar
  26. 26.
    The ubiris v2: a database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1529–1535 (2007)Google Scholar
  27. 27.
    Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2002)CrossRefGoogle Scholar
  28. 28.
    Yoon, S., Bae, K., Ryoung, K., Kim, P.: Pan-tilt-zoom based iris image capturing system for unconstrained user environments at a distance. Lect. Notes Comput. Sci. 4642, 653–662 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.University of Beira InteriorCovilhãPortugal

Personalised recommendations