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Detection of iris in images using brightness gradient projections

  • Proceedings of IMTA-3-2010
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Abstract

A method is proposed to detect human iris location and size in digital image given some point lying inside the pupil. The method is based on construction of histograms, or projections of local brightness gradients, and combinations of projections’ maxima being regarded as possible locations of pupil and iris borders. The method is notable for its low computational complexity and high tolerance to noise.

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References

  1. T. Maenpaa, “An Iterative Algorithm for Fast Iris Detection,” in Proc. IWBRS 2005 (Beijing Press, Beijing), pp. 127–134.

  2. B. Lipinski, Iris Recognition: Detecting the Pupil, Available from: http//cnx.org/content/m12487/latest/.

  3. J. Daugman, “High Confidence Personal Identification by Rapid Video Analysis of Iris Texture,” in Proc. IEEE Int. Carnahan Conf. on Security Technology (Madrid, 1992), pp. 50–60.

  4. R. P. Wildes, J. C. Asmuth, G. L. Green, et al., “A System for Automated Iris Recognition,” in Proc. 2nd IEEE Workshop on Applications of Computer Vision (Sarasota, FL, 1994), pp. 121–128.

  5. R. O. Duda and P. E. Hart, “Use of the Hough Transformation to Detect Lines and Curves in Pictures,” Comm. ACM 15, 11–15 (1972).

    Article  Google Scholar 

  6. D. E. Ben, M. S. Nixon, and J. N. Carter, “Robust Eye Centre Extraction Using the Hough Transform,” in Proc. 1st Int. Conf. on Audio- and Video-Based Biometric Person Authentication (Crans-Montana, 1997).

  7. C. Kimme, D. Dallard, and J. Sklansky, “Finding Circles by Array of Accumulators,” Comm. ACM 18, 120–122 (1975).

    Article  MATH  Google Scholar 

  8. E. R. Davies, “A High Speed Algorithm for Circular Object Location,” Patten Recogn. Lett. 6, 323–333 (1987).

    Article  Google Scholar 

  9. I. A. Matveev, “Method of Detection of Circle with Known Internal Point,” Trans. Inst. Syst. Anal. Russ. Acad. Sci. Dynam. Heterogeneous Syst. 31(1), 288–293 (2007).

    Google Scholar 

  10. A. A. Rad, K. Faez, and N. Qaragozlou, “Fast Detection Using Gradient Pair Vectors,” in Proc. VII Digital Image Computing: Techniques and Applications. Dec. 10–12, 2003, Sydney, Ed. by C. Sun, H. Talbot, S. Oerselin, and T. Adriaansen (Sydney, 2003).

  11. C. Sun and S. Pallottino, “Circular Shortest Path in Images,” Pattern Recogn. 36(3), 709–719 (2003).

    Article  Google Scholar 

  12. T.-C. Chen and K.-L. Chung, “An Efficient Randomized Algorithm for Detecting Circles,” Comput. Vision Image Understand. 83, 172–191 (2001).

    Article  MATH  Google Scholar 

  13. J. J. Kansky, Clinical Ophthalmology: A Systematic Approach, 5th ed. (Elsevier, London, 2003).

    Google Scholar 

  14. L. Masek and P. Kovesi, MATLAB Source Code for a Biometric Identification System Based on Iris Patterns (The School of Computer Science and Software Engineering, The University of Western Australia, 2003).

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Authors and Affiliations

  1. Dorodnicyn Computing Center, Russian Academy of Sciences, ul. Vavilova 40, Moscow, 119333, Russia

    I. A. Matveev

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  1. I. A. Matveev
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Correspondence to I. A. Matveev.

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Ivan Matveev. Master’s degree at the Moscow Institute of Physics and Technology in 1997. In 1999 PhD in applied mathematics, Computer Centre, Russian Academy of Sciences. Since 2004 head of the Intellectual Systems sector of the Complex Systems department, Computer Centre, Russian Academy of Sciences. Research interests: biometric identification, face and iris recognition, and real-time image processing.

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Matveev, I.A. Detection of iris in images using brightness gradient projections. Pattern Recognit. Image Anal. 21, 41–44 (2011). https://doi.org/10.1134/S105466181101010X

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  • DOI: https://doi.org/10.1134/S105466181101010X

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