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A Comparative Study of Robust Segmentation Algorithms for Iris Verification System of High Reliability

  • Mireya S. García-Vázquez
  • Eduardo Garea-Llano
  • Juan M. Colores-Vargas
  • Luis M. Zamudio-Fuentes
  • Alejandro A. Ramírez-Acosta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9116)

Abstract

Iris recognition is being widely used in different environments where the identity of a person is necessary. Therefore, it is a challenging problem to maintain high reliability and stability of this kind of systems in harsh environments. Iris segmentation is one of the most important process in iris recognition to preserve the above-mentioned characteristics. Indeed, iris segmentation may compromise the performance of the entire system. This paper presents a comparative study of four segmentation algorithms in the frame of the high reliability iris verification system. These segmentation approaches are implemented, evaluated and compared based on their accuracy using three unconstraint databases, one of them is a video iris database. The result shows that, for an ultra-high security system on verification at FAR = 0.01 %, segmentation 3 (Viterbi) presents the best results.

Keywords

Iris recognition Segmentation Uncontrolled environments 

Notes

Aknowledgment

This research was supported by SIP2015 project grant from Instituto Politécnico Nacional from México and Iris Project grant from Advanced Technologies Application Center from Cuba.

References

  1. 1.
    Cao, Y., Wang, Z., Lv, Y.: Genetic algorithm based parameter identification of defocused image. In: ICCCSIT 2008, International Conference on Computer Science and Information Technology, pp. 439–442, September 2008Google Scholar
  2. 2.
    Colores, J.M., García-Vázquez, M., Ramírez-Acosta, A., Pérez-Meana, H.: Iris image evaluation for non-cooperative biometric iris recognition system. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011, Part II. LNCS, vol. 7095, pp. 499–509. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Daugman, J.: The importance of being random: statistical principles of iris recognition. Pattern Recogn. 36, 279–291 (2003)CrossRefGoogle Scholar
  4. 4.
    Phillips, P., Scruggs, W., Toole, A.: FRVT 2006 and ICE 2006 large-scale results, Technical report, National Institute of Standards and Technology, NISTIR 7408 (2007)Google Scholar
  5. 5.
    Proenca, H., Alexandre, L.: The NICE.I: noisy iris challenge evaluation. In: Proceedings of the IEEE First International Conference on Biometrics: Theory, Applications and Systems, vol. 1, pp. 1–4 (2007)Google Scholar
  6. 6.
    Newton, E.M., Phillips, P.J.: Meta-analysis of third-party evaluations of iris recognition. IEEE Trans. Syst. Man Cybern. 39(1), 4–11 (2009)CrossRefGoogle Scholar
  7. 7.
    Kalka, N.D., Zuo, J., Schmid, N.A., Cukic, B.: Image quality assessment for iris biometric. In: SPIE 6202: Biometric Technology for Human Identification III, vol. 6202, pp. D1–D11 (2006)Google Scholar
  8. 8.
    Chen, Y., Dass, S.C., Jain, A.K.: Localized iris image quality using 2-d wavelets. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 373–381. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Belcher, C., Du, Y.: A selective feature information approach for iris image quality measure. IEEE Trans. Inf. Forensics Secur. 3(3), 572–577 (2008)CrossRefGoogle Scholar
  10. 10.
    Sanchez-Gonzalez, Y., Chacon-Cabrera, Y., Garea-Llano, E.: A comparison of fused segmentation algorithms for iris verification. In: Bayro-Corrochano, E., Hancock, E. (eds.) CIARP 2014. LNCS, vol. 8827, pp. 112–119. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  11. 11.
    Sutra, G., Garcia-Salicetti, S., Dorizzi, B.: The viterbi algorithm at different resolutions for enhanced iris segmentation. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 310–316. IEEE (2012)Google Scholar
  12. 12.
    Masek, L.: Recognition of human iris patterns for biometric identification. Technical report (2003)Google Scholar
  13. 13.
    Uhl, A., Wild, P.: Weighted adaptive hough and ellipsopolar transforms for realtime iris segmentation. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 283–290. IEEE (2012)Google Scholar
  14. 14.
    Wildes, R.P., Asmuth, J.C., Green, G.L.: A system for automated recognition 0-8186-6410-X/94, IEEE (1994)Google Scholar
  15. 15.
    Canny, J.F.: Finding edges and lines in images. M.S. thesis, Massachusetts Institute of Technology (1983)Google Scholar
  16. 16.
    Hough, P.V.C.: Method and means for recognizing complex patterns. U.S. Patent 3 069 654 (1962)Google Scholar
  17. 17.
    Colores-Vargas, J.M., García-Vázquez, M., Ramírez-Acosta, A., Pérez-Meana, H., Nakano-Miyatake, M.: Video images fusion to improve iris recognition accuracy in unconstrained environments. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Rodríguez, J.S., di Baja, G.S. (eds.) MCPR 2012. LNCS, vol. 7914, pp. 114–125. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  18. 18.
    CASIA-V3-Interval. The Center of Biometrics and Security Research, CASIA Iris Image Database. http://biometrics.idealtest.org/
  19. 19.
    CASIA-V4-Thousands. The Center of Biometrics and Security Research, CASIA Iris Image Database. http://biometrics.idealtest.org/
  20. 20.
    Multiple Biometric Grand Challenge. http://face.nist.gov/mbgc/
  21. 21.
    Bowyer, K.W., Hollingsworth, K., Flynn, P.J.: Image understanding for iris biometrics: a survey. Comput. Vis. Image Underst. 110(2), 281–307 (2008)CrossRefGoogle Scholar
  22. 22.
    Zweig, M., Campbell, G.: Receiver-operating characteristic ROC plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39, 561–577 (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mireya S. García-Vázquez
    • 1
  • Eduardo Garea-Llano
    • 2
  • Juan M. Colores-Vargas
    • 3
  • Luis M. Zamudio-Fuentes
    • 1
  • Alejandro A. Ramírez-Acosta
    • 4
  1. 1.Instituto Politécnico Nacional - CITEDITijuanaMexico
  2. 2.Advanced Technologies Application Center - CENATAVHabanaCuba
  3. 3.Universidad Autónoma de Baja California - CITECTijuanaMexico
  4. 4.MIRAL R&D&IHoustonUSA

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