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An Iterative Algorithm for Fast Iris Detection

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Advances in Biometric Person Authentication (IWBRS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3781))

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Abstract

Reliable detection of irises is a necessary precondition for any iris-based biometric identification system. It is also important in measuring the movement of eyes in applications such as alertness detection and eye-guided user interfaces. This paper introduces a novel iterative algorithm for the accurate and fast localization of irises in eye images.

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References

  1. Casia iris image database, http://www.sinobiometrics.com

  2. Boles, W.W., Boashash, B.: A human identification technique using images of the iris and wavelet transform. IEEE Transactions on Signal Processing 46(4), 1185–1188 (1998)

    Article  Google Scholar 

  3. Daugman, J.: How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 21–30 (2004)

    Article  Google Scholar 

  4. Huang, J., Wang, Y., Tan, T., Cui, J.: A new iris segmentation method for recognition. In: 19th International Conference on Pattern Recognition, vol. 3, pp. 554–557 (2004)

    Google Scholar 

  5. Kass, M., Witkin, A., Terzopoulo, D.: Snakes: active contour models. International Journal of Computer Vision 1(4), 321–331 (1987)

    Article  Google Scholar 

  6. Lawson, C., Hanson, R.: Solving Least Squares Problems. Prentice-Hall, Englewood Cliffs (1974)

    MATH  Google Scholar 

  7. Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal identification based on iris texture analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12), 1519–1533 (2003)

    Article  Google Scholar 

  8. Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient iris recognition by characterizing key local variations. IEEE Transactions on Image Processing 13(6), 739–750 (2004)

    Article  Google Scholar 

  9. Vatsa, M., Singh, R., Gupta, P.: Comparison of iris recognition algorithms. In: International Conference on Intelligent Sensing and Information Processing, Chennai, India, pp. 354–358 (2004)

    Google Scholar 

  10. Wildes, R., Asmuth, J., Green, G., Hsu, S., Kolczynski, R., Matey, J., McBride, S.: A machine-vision system for iris recognition. Machine Vision and Applications 9, 1–8 (1996)

    Article  Google Scholar 

  11. Wu, Y., Liu, H., Zha, H.: A new method of detecting human eyelids based on deformable templates. Systems, Man and Cybernetics 1, 604–609 (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Machine Vision Group, Infotech Oulu, University of Oulu, Finland

    Topi Mäenpää

Authors
  1. Topi Mäenpää
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Editor information

Editors and Affiliations

  1. Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences,  

    Stan Z. Li

  2. National Laboratory of Pattern Recognition, Institute of Automation, CAS,  

    Zhenan Sun

  3. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  4. Exploratory Computer Vision Group, IBM T.J. Watson Research Center, 10598, Yorktown Heights, NY

    Sharath Pankanti

  5. CNRS LTCI/TSI Paris, 46 rue Barrault, 75634, Paris Cedex 13, France

    Gérard Chollet

  6. Biometrics Research Centre, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    David Zhang

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© 2005 Springer-Verlag Berlin Heidelberg

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Mäenpää, T. (2005). An Iterative Algorithm for Fast Iris Detection. In: Li, S.Z., Sun, Z., Tan, T., Pankanti, S., Chollet, G., Zhang, D. (eds) Advances in Biometric Person Authentication. IWBRS 2005. Lecture Notes in Computer Science, vol 3781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569947_16

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  • DOI: https://doi.org/10.1007/11569947_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29431-3

  • Online ISBN: 978-3-540-32248-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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