Video Images Fusion to Improve Iris Recognition Accuracy in Unconstrained Environments

  • Juan M. Colores-Vargas
  • Mireya García-Vázquez
  • Alejandro Ramírez-Acosta
  • Héctor Pérez-Meana
  • Mariko Nakano-Miyatake
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7914)


To date, research on the iris recognition systems are focused on the optimization and proposals of new stages for uncontrolled environment systems to improve the recognition rate levels. In this paper we propose to exploit the biometric information from video-iris, creating a fusioned normalized template through an image fusion technique. Indeed, this method merges the biometric features of a group of video images getting an enhanced image which therefore improves the recognition rates iris, in terms of Hamming distance, in an uncontrolled environment system. We analyzed seven different methods based on pixel-level and multi-resolution fusion techniques on a subset of images from the MBGC.v2 database. The experimental results show that the PCA method presents the best performance to improve recognition values according to the Hamming distances in 83% of the experiments.


Fusion Iris MBGC PCA Recognition 


  1. 1.
    Daugman, J.: How Iris Recognition Works. IEEE Transactions on Circuits and Systems for Video Technology 14, 21–30 (2004)CrossRefGoogle Scholar
  2. 2.
    Wildes, R.: Iris Recognition: An Emerging Biometric Technology. Proceedings of the IEEE 85(9), 1348–1363 (1997)CrossRefGoogle Scholar
  3. 3.
    Gamassi, M., Lazzaroni, M., Misino, M., Piuri, V.: Quality assessment of biometric systems: a comprehensive perspective based on accuracy and performance measurement. IEEE Trans. Instrum. Meas. 54, 1489–1496 (2005)CrossRefGoogle Scholar
  4. 4.
    Vatsa, M., Singh, R., Gupta, P.: Comparison of Iris Recognition. In: International Conference on Intelligent Sensing and Information Processing, pp. 354–358 (2004)Google Scholar
  5. 5.
    Hollingsworth, K., Peters, T., Bowyer, K.: Iris recognition using signal-level fusion of frames from video. IEEE Transa. Inform. Forensics Secur. 4(4), 837–848 (2009)CrossRefGoogle Scholar
  6. 6.
    Lee, Y., Phillips, P., Michaels, R.: An automated video-based system for iris recognition. In: Proc. Int. Conf. Biom., pp. 1–8 (2009)Google Scholar
  7. 7.
    Colores-Vargas, J., García, M., Ramírez, A., García, M., Nakano, M., Perez, H.: Iris recognition system based on video for unconstrained environments. Scientific Research and Essays 7(35), 3114–3127 (2012)CrossRefGoogle Scholar
  8. 8.
    Jillela, R., Ross, A., Flynn, P.: Information Fusion in Low-resolution Iris Videos Using Principal Components Transform. In: Proceedings of IEEE Workshop on Applications of Computer Vision (WACV), Kona, USA (January 2011)Google Scholar
  9. 9.
    Mitchell, H., Singh, R., Gupta, P.: Multifocus Method for Controlling Depth of Field. Grafica Obscura (1994)Google Scholar
  10. 10.
    Haeberli, P., Singh, R., Gupta, P.: Image Fusion: Theories, Techniques and Applications. Springer, Heidelberg 2010 (2004)Google Scholar
  11. 11.
    Pajares, G., De la Cruz, J.: Visión por Computador: Imágenes Digitales y Aplicaciones. RA-MA, Madrid (2001)Google Scholar
  12. 12.
    Burt, P., Kolczynski, R.: Enhanced image capture through fusion. In: Proc. Fourth Int. Conf. on Computer Vision, pp. 173–182 (1993)Google Scholar
  13. 13.
    Zhang, Z., Blum, R.: A categorization of Multiscale-Decomposition-Based Image Fusion Schemes with a Performance Study for a Digital Camera Application. Proc. IEEE 87(8), 1315–1326 (1999)CrossRefGoogle Scholar
  14. 14.
    Toet, A., van Ruyven, J., Valeton, J.: Merging termal and visual images by a contrast pyramid. Optical Engineering 28(7), 789–792 (1989)CrossRefGoogle Scholar
  15. 15.
    Anderson, H.: A filter-subtract-decimate hierarchical pyramid signal analyzing and synthesizing technique. U.S. Patent 4.718 104 (1987)Google Scholar
  16. 16.
    Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989)zbMATHCrossRefGoogle Scholar
  17. 17.
    Multiple Biometric Grand Challenge,
  18. 18.
    Colores-Vargas, J.M., García-Vázquez, M.S., Ramírez-Acosta, A.A.: Measurement of defocus level in iris images using convolution kernel method. 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
  19. 19.
    Masek, L.: Recognition of human iris patterns for biometric identification. Master’s thesis, University of Western Australia (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Juan M. Colores-Vargas
    • 1
  • Mireya García-Vázquez
    • 1
  • Alejandro Ramírez-Acosta
    • 2
  • Héctor Pérez-Meana
    • 3
  • Mariko Nakano-Miyatake
    • 3
  1. 1.Instituto Politécnico NacionalCITEDITijuanaMéxico
  2. 2.MIRAL R&DImperial BeachUSA
  3. 3.ESIMEInstituto Politécnico NacionalDF. MéxicoMexico

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