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Nonideal Iris Recognition Using Level Set Approach and Coalitional Game Theory

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Computer Vision Systems (ICVS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5815))

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Abstract

This paper presents an efficient algorithm for iris recognition using the level set approach and the coalitional game theory. To segment the inner boundary from a nonideal iris image, we apply a level set based curve evolution approach using the edge stopping function, and to detect the outer boundary, we employ the curve evolution approach using the regularized Mumford-Shah segmentation model with an energy minimization approach. An iterative algorithm, called the Contribution-Selection Algorithm (CSA), in the context of coalitional game theory is used to select the optimal features subset without compromising the accuracy. The verification performance of the proposed scheme is validated using the UBIRIS Version 2, the ICE 2005, and the WVU datasets.

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References

  1. Daugman, J.: How iris recognition works. IEEE Transaction on Circuits, Systems and Video Technology 14(1), 1–17 (2003)

    Google Scholar 

  2. Daugman, J.: New methods in iris recognition. IEEE Transactions on Systems, Man, and Cybernetics-Part B 37(5), 1167–1175 (2007)

    Article  Google Scholar 

  3. Schuckers, S.A.C., Schmid, N.A., Abhyankar, A., Dorairaj, V., Boyce, C.K., Hornak, L.A.: On techniques for angle compensation in nonideal iris recognition. IEEE Transactions on Systems, Man, and Cybernetics-Part B 37(5), 1176–1190 (2007)

    Article  Google Scholar 

  4. Vatsa, M., Singh, R., Noore, A.: Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Transactions on Systems, Man, and Cybernetics-Part B 38(4), 1021–1035 (2008)

    Article  Google Scholar 

  5. Ross, A., Shah, S.: Segmenting non-ideal irises using geodesic active contours. In: Biometric Consortium Conference, IEEE Biometrics symposium, pp. 1–6 (2006)

    Google Scholar 

  6. Osher, S., Sethian, J.A.: Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulation. Journal of Computational Physics 79, 12–49 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  7. Malladi, R., Sethian, J., Vemuri, B.: Shape modelling with front propagation: a level set approach. IEEE Transaction on Pattern Analysis and Machine Intelligence 17(2), 158–174 (1995)

    Article  Google Scholar 

  8. Sethian, J.A., Strain, J.: Crystal growth and dendritic solidi cation. Journal of Computational Physics 98, 231–253 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  9. Chan, T., Vese, L.: Active contours without edges. IEEE Transaction on Image Processing 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  10. Mumford, D., Shah, J.: Optimal approximation by piecewise smooth functions and associated variational problems. Communication Pure Applied Math. 42, 577–685 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  11. Iris Challenge Evaluation (ICE) dataset found, http://iris.nist.gov/ICE/

  12. Iris Dataset obtained from West Virginia University (WVU), http://www.wvu.edu/

  13. UBIRIS dataset obtained from department of computer science, University of Beira Interior, Portugal., http://iris.di.ubi.pt/

  14. Roy, K., Bhattacharya, P.: Iris recognition based on zigzag collarette region and asymmetrical support vector machines. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 854–865. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  15. Roy, K., Bhattacharya, P.: Optimal features subset selection using genetic algorithms for iris recognition. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 894–904. Springer, Heidelberg (2008)

    Google Scholar 

  16. Roy, K., Bhattacharya, P.: Adaptive Asymmetrical SVM and Genetic Algorithms Based Iris Recognition. In: International Conference on Pattern Recognition ICPR 2008, pp. 1–4 (2008)

    Google Scholar 

  17. Cohen, S., Dror, D., Ruppin, E.: Feature selection via coalitional game theory. In: Neural Computation, vol. 19, pp. 1939–1961. MIT press, Cambridge (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Quebec, Canada

    Kaushik Roy & Prabir Bhattacharya

Authors
  1. Kaushik Roy
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  2. Prabir Bhattacharya
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Editor information

Editors and Affiliations

  1. ICSI & UC Berkeley, 1947 Center Street, Suite 600, 94704, Berkeley, CA, USA

    Mario Fritz

  2. Department of Computer Science, TU Darmstadt, Hochschulstrasse 10, 64289, Darmstadt, Germany

    Bernt Schiele

  3. Department of Electrical Engineering and Computer Science, Montefiore Institute, University of Liège, B28, Sart Tilman Campus, Grand Traverse 10, 4000, Liège, Belgium

    Justus H. Piater

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© 2009 Springer-Verlag Berlin Heidelberg

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Roy, K., Bhattacharya, P. (2009). Nonideal Iris Recognition Using Level Set Approach and Coalitional Game Theory. In: Fritz, M., Schiele, B., Piater, J.H. (eds) Computer Vision Systems. ICVS 2009. Lecture Notes in Computer Science, vol 5815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04667-4_40

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  • DOI: https://doi.org/10.1007/978-3-642-04667-4_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04666-7

  • Online ISBN: 978-3-642-04667-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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