Local Quality Method for the Iris Image Pattern

  • Luis Miguel Zamudio-Fuentes
  • Mireya S. García-Vázquez
  • Alejandro Alvaro Ramírez-Acosta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)


Recent researches on iris recognition without user cooperation have introduced video-based iris capturing approach. Indeed, it provides more information and more flexibility in the image acquisition stage for noncooperative iris recognition systems. However, a video sequence can contain images with different level of quality. Therefore, it is necessary to select the highest quality images from each video to improve iris recognition performance. In this paper, we propose as part of a video quality assessment module, a new local quality iris image method based on spectral energy analysis. This approach does not require the iris region segmentation to determine the quality of the image such as most of existing approaches. In contrast to other methods, the proposed algorithm uses a significant portion of the iris region to measure the quality in that area. This method evaluates the energy of 1000 images which were extracted from 200 iris videos from the MBGC NIR video database. The results show that the proposed method is very effective to assess the quality of the iris information. It obtains the highest 2 images energies as the best 2 images from each video in 226 milliseconds.


Iris recognition biometrics video quality assessment 


  1. 1.
    Kang, B.J., Park, K.R.: A study on fast iris restoration based on focus checking. In: Perales, F.J., Fisher, R.B. (eds.) AMDO 2006. LNCS, vol. 4069, pp. 19–28. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Zamudio-Fuentes, L.M., García-Vázquez, M.S., Ramírez-Acosta, A.A.: Iris Segmentation Using a Statistical Approach. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Kittler, J. (eds.) MCPR 2010. LNCS, vol. 6256, pp. 164–170. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    García-Vázquez, M., Ramírez-Acosta, A.: Person verification process using iris information. Research in Computing Science 44, 97–104 (2009)Google Scholar
  4. 4.
    Zamudio, L.M.: Reconocimiento del iris como identificación biométrica utilizando el video. MSc thesis, IPN (January 2011)Google Scholar
  5. 5.
    Chen, Y., Dass, S.C., Jain, A.K.: Localized iris image quality using 2-d wavelets. In: ICB 2006 (2006)Google Scholar
  6. 6.
    Huang, Y., Ma, Z., Xie, M.: Rapid and effective method of quality assessment on sequence iris image. In: MIPPR 2007. Proc. of SPIE, vol. 6786 (2007)Google Scholar
  7. 7.
    Colores-Vargas, J.M., García-Vázquez, M.S., Ramírez-Acosta, A.A.: Measurement of defocus level in iris images using different convolution kernel methods. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Kittler, J. (eds.) MCPR 2010. LNCS, vol. 6256, pp. 125–133. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Kang, B.J., Park, K.R.: Real-time image restoration for iris recognition Systems. IEEE Trans. on Systems 37(6), 1555–1566 (2007)Google Scholar
  9. 9.
    Kang, B.J., Park, K.R.: A study on restoration of iris images with motion-and-optical blur on mobile iris recognition devices. Wiley Periodicals (2009)Google Scholar
  10. 10.
    Lee, Y., Phillips, P.J., Micheals, R.J.: An Automated Video-Based System for Iris Recognition. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 1160–1169. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Daugman, J.G.: How iris recognition works. IEEE Trans. Circ. Syst. Video Tech. 14(1), 21–30 (2004)CrossRefGoogle Scholar
  12. 12.
    Gonzalez, R.C., Woods, R.: Digital image processing. Addison-Wesley (1996)Google Scholar
  13. 13.
  14. 14.
    Multi Biometric Grand Challenge,
  15. 15.
  16. 16.
    Chinese Academy of Sciences, Institute of Automation (CASIA),

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Luis Miguel Zamudio-Fuentes
    • 1
  • Mireya S. García-Vázquez
    • 1
  • Alejandro Alvaro Ramírez-Acosta
    • 2
  1. 1.Centro de Investigación y Desarrollo de Tecnología Digital (CITEDI-IPN)TijuanaMéxico
  2. 2.MIRAL. R&DUSA

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