Published January 4, 2015 | Version 10000519

Size-Reduction Strategies for Iris Codes

Description

Iris codes contain bits with different entropy. This
work investigates different strategies to reduce the size of iris
code templates with the aim of reducing storage requirements and
computational demand in the matching process. Besides simple subsampling
schemes, also a binary multi-resolution representation as
used in the JBIG hierarchical coding mode is assessed. We find that
iris code template size can be reduced significantly while maintaining
recognition accuracy. Besides, we propose a two-stage identification
approach, using small-sized iris code templates in a pre-selection
stage, and full resolution templates for final identification, which
shows promising recognition behaviour.

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References

  • M.J. Burge and K. Bowyer, editors. Handbook of Iris Recognition. Springer-Verlag, 2013.
  • J. E. Gentile, N. Ratha, and J. Connell. SLIC: Short Length Iris Code. In BTAS'09: Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems, pages 171–175, Piscataway, NJ, USA, 2009. IEEE Press.
  • K. P. Hollingsworth, K. W. Bowyer, and P. J. Flynn. The best bits in an iris code. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(6):964–973, 2009.
  • Mario Konrad, Herbert St¨ogner, Andreas Uhl, and Peter Wild. Computationally efficient serial combination of rotation-invariant and rotation compensating iris recognition algorithms. In P. Richard and J. Braz, editors, Proceedings of the 5th International Conference on Computer Vision Theory and Applications, VISAPP'10, volume 1, pages 85–90, Angers, France, May 2010.
  • L. Ma, T. Tan, Y. Wang, and D. Zhang. Efficient iris recognition by characterizing key local variations. IEEE Transactions on Image Processing, 13:739–750, 2004.
  • C.-H. Park and J.-J. Lee. Extracting and combining multimodal iris features. In Proceedings of the 1st IAPR International Conference on Biometrics (ICB'06), number 3832 in Lecture Notes on Computer Science, pages 389–396, 2006.
  • R. Pfarrhofer and A. Uhl. Selective image encryption using JBIG. In J. Dittmann, S. Katzenbeisser, and A. Uhl, editors, Communicationa and Multimedia Security (Proceedings of CMS 2005), volume 3677 of Lecture Notes on Computer Science, pages 98–107, Salzburg, Austria, September 2005. Springer-Verlag.
  • Christian Rathgeb, Andreas Uhl, and Peter Wild. Incremental iris recognition: A single-algorithm serial fusion strategy to optimize time complexity. In Proceedings of the 4th IEEE International Conference on Biometrics: Theory, Application, and Systems 2010 (IEEE BTAS'10), pages 1–6, Washington DC, DC, USA, September 2010. IEEE Press.
  • Christian Rathgeb, Andreas Uhl, and Peter Wild. Iris Recognition: From Segmentation to Template Security, volume 59 of Advances in Information Security. Springer Verlag, 2013. [10] Z. Sun, Y. Wang, T. Tan, and J. Cui. Improving iris recognition accuracy via cascaded classifiers. IEEE Transactions on Systems, Man and Cybernetics, 35(3):435–441, 2005. [11] P.-F. Zhang, D.-S. Li, and Q. Wang. A novel iris recognition method based on feature fusion. In Proceedings of the International Conference on Machine Learning and Cybernetics, pages 3661–3665, 2004.