A Contrario Detection of False Matches in Iris Recognition

  • Marcelo Mottalli
  • Mariano Tepper
  • Marta Mejail
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)


The pattern of the human iris contains rich information which provides one of the most accurate methods for recognition of individuals. Identification through iris recognition is achieved by matching a biometric template generated from the texture of the iris against an existing database of templates. This relies on the assumption that the probability of two different iris generating similar templates is very low. This assumption opens a question: how can one be sure that two iris templates are similar because they were generated from the same iris and not because of some other random factor?

In this paper we introduce a novel technique for iris matching based on the a contrario framework, where two iris templates are decided to belong to the same iris according to the unlikelyness of the similarity between them. This method provides an intuitive detection thresholding technique, based on the probability of occurence of the distance between two templates. We perform tests on different iris databases captured in heterogeneous environments and we show that the proposed identification method is more robust than the standard method based on the Hamming distance.


False Alarm Equal Error Rate Iris Recognition False Match Biometric Template 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Smart sensors iris database,
  2. 2.
    Bowyer, K., Hollingsworth, H., Flynn, P.: Image understanding for iris biometrics: A survey. Computer Vision and Image Understanding 110, 281–307 (2007)CrossRefGoogle Scholar
  3. 3.
    Cao, F., Lisani, J.L., Morel, J.M., Musé, P., Sur, F.: A Theory of Shape Identification, vol. 1948. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  4. 4.
    Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)CrossRefGoogle Scholar
  5. 5.
    Gentile, J.E., Ratha, N., Connell, J.: Slic: Short-length iris codes. In: IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, BTAS 2009, pp. 1–5 (September 2009)Google Scholar
  6. 6.
    Hollingsworth, K., Bowyer, K., Flynn, P.: The best bits in an iris code. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 1–10 (2009)CrossRefGoogle Scholar
  7. 7.
    Hollingsworth, K., Peters, T., Bowyer, K., Flynn, P.: Iris recognition using signal-level fusion of frames from video. IEEE Transactions on Information Forensics and Security 4(4), 837–848 (2009)CrossRefGoogle Scholar
  8. 8.
    Mottalli, M., Mejail, M., Jacobo-Berlles, J.: Flexible image segmentation and quality assessment for real-time iris recognition. In: 16th IEEE International Conference on Image Processing (ICIP), pp. 1941–1944 (November 2009)Google Scholar
  9. 9.
    Proena, H., Alexandre, L.: Toward non-cooperative iris recognition: A classification approach using multiple signatures. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4), 607–612 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marcelo Mottalli
    • 1
  • Mariano Tepper
    • 1
  • Marta Mejail
    • 1
  1. 1.Departamento de ComputaciónUniversidad de Buenos AiresArgentina

Personalised recommendations