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GESLIC: genetic and evolutionary-based short-length iris codes

Published: 24 March 2011 Publication History
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  • Abstract

    The discriminative potential of the human iris has been the center of much attention in the past decade. Feature selection has proven to be an effective means of increasing performance of current iris recognition systems. This paper introduces a novel method of reducing the size of iris codes by the use of genetic & evolutionary computing to eliminate bits corresponding to entire rings of the iris.

    References

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    R. M. Bolle, S. Pankanti, J. H. Connel, and N. K. Ratha. "Iris individuality: A partial iris model," in Proc. ICPR '04, 2004, p. 927.
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    J. Daugman. "How iris recognition works," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, pp. 21--30, Jan. 2004.
    [3]
    J. E. Gentile, N. Ratha, and J. Connell. "SLIC: Short-length iris codes," in Proc. BTAS'09, 2009.
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    J. E. Gentile, N. Ratha, and J. Connell. "An efficient, two-stage iris recognition system," in Proc. BTAS'09, 2009.
    [5]
    Karen Hollingsworth, Kevin Bowyer, Patrick Flynn (2007). "All iris code bits are not created equal", 2007 IEEE Conference on Biometrics: Theory, Applications, and Systems, September 2007.
    [6]
    Dozier, G., Bryant, K., Savvides, M., and Munemoto, T. (2010). "GRIT: Genetically Revised Iris Templates for Iris Recognition," (to appear in) The Proceedings of the 2010 International Conference Genetic and Evolutionary Methods, (GEM'10: July 12--15, 2010, Las Vegas, USA).
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    Dozier, G., Homaifar, A., Tunstel, E., and Battle, D. (2001). "An Introduction to Evolutionary Computation" (Chapter 17), Intelligent Control Systems Using Soft Computing Methodologies, A. Zilouchian & M. Jamshidi (Eds.), pp. 365--380, CRC press.
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    Larranaga, P. and Lozano, J. A., Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, 2002.}
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    "Iris Challenge Evaluation." (2006) National Institute of Standards and Technology. {Online}. Available: http://iris.mst.gov/ICE/.
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    Thornton, Savvides, M., and Kumar, V. (2005). "Robust Iris Recognition Using Advanced Correlation Techniques," Proc. Int'l Conf. Image Analysis and Recognition, pp. 1098--1105, 2005.

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    • (2012)Genetic and evolutionary methods for biometric feature reductionInternational Journal of Biometrics10.1504/IJBM.2012.0476424:3(220-245)Online publication date: 1-Jul-2012

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    1. GESLIC: genetic and evolutionary-based short-length iris codes

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      cover image ACM Conferences
      ACMSE '11: Proceedings of the 49th annual ACM Southeast Conference
      March 2011
      399 pages
      ISBN:9781450306867
      DOI:10.1145/2016039
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 24 March 2011

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      Author Tags

      1. biometrics
      2. feature selection
      3. genetic & evolutionary computing
      4. short-length iris code

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      ACM SE '11
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      ACM SE '11: ACM Southeast Regional Conference
      March 24 - 26, 2011
      Georgia, Kennesaw

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      Overall Acceptance Rate 502 of 1,023 submissions, 49%

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      • (2012)Genetic and evolutionary methods for biometric feature reductionInternational Journal of Biometrics10.1504/IJBM.2012.0476424:3(220-245)Online publication date: 1-Jul-2012

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