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Iris Recognition in the Visible Wavelength

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Handbook of Iris Recognition

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

Abstract

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.

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References

  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. 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. Bowyer, K., Hollingsworth, K., Flynn, P.: Image understanding for iris biometrics: a survey. Comput. Vis. Image Underst. 110, 281–307 (2008)

    Article  Google Scholar 

  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. Cambier, J.: Iridian large database performance. Tech. rep., Iridian Technologies (2007)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  8. Daugman, J.: Biometric decision landscapes. Tech. rep. TR482, University of Cambridge Computer Laboratory (2000)

    Google Scholar 

  9. Daugman, J.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)

    Article  Google Scholar 

  10. Daugman, J.: Probing the uniqueness and randomness of iriscodes: results from 200 billion iris pair comparisons. Proc. IEEE 94, 1927–1935 (2006)

    Article  Google Scholar 

  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. 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. Honeywell International Inc.: A distance iris recognition. United States Patent 0036397 (2007)

    Google Scholar 

  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. International Biometric Group: Independent test of iris recognition technology (2005)

    Google Scholar 

  16. Mansfield, T., Kelly, G., Chandler, D., Kane, J.: Biometric product testing final report. National Physical Laboratory, Middlesex (2001)

    Google Scholar 

  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. 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. Narayanswamy, R., Johnson, G., Silveira, P., Wach H.: Extending the imaging volume for biometric iris recognition. Appl. Opt. 44(5), 701–712 (2005)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  21. Park, K., Kim, J.: A real-time focusing algorithm for iris recognition camera. IEEE Trans. Syst. Man Cybern. 35(3), 441–444 (2005)

    Article  Google Scholar 

  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. 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. Sarna, T., Meredith, P.: The physical and chemical properties of eumelanin. Pigment Cell Res. 19, 572–594 (2010)

    Google Scholar 

  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. 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. Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2002)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Acknowledgements

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.

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Authors and Affiliations

  1. University of Beira Interior, Covilhã, Portugal

    Hugo Proença

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  1. Hugo Proença
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Correspondence to Hugo Proença .

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Editors and Affiliations

  1. MITRE Corporation, Colshire Drive 7515, McLean, 22102-7539, Virginia, USA

    Mark J. Burge

  2. , Department of Computer Science and Engin, University of Notre Dame, Fitzpatrick Hall 384, Notre Dame, 46556-5637, Indiana, USA

    Kevin W. Bowyer

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Proença, H. (2013). Iris Recognition in the Visible Wavelength. In: Burge, M., Bowyer, K. (eds) Handbook of Iris Recognition. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4402-1_8

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  • DOI: https://doi.org/10.1007/978-1-4471-4402-1_8

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4401-4

  • Online ISBN: 978-1-4471-4402-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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