Encyclopedia of Biometrics

Living Edition
| Editors: Stan Z. Li, Anil K. Jain

Anti-spoofing: Iris Databases

Living reference work entry
DOI: https://doi.org/10.1007/978-3-642-27733-7_9050-2

Synonyms

Definition

Anti-spoofing may be defined as the pattern recognition problem of automatically differentiating between real and fake biometric samples produced with a synthetically manufactured artifact (e.g., iris photograph or plastic eye). As with any other machine learning problem, the availability of data is a critical factor in order to successfully address this challenging task. Furthermore, such data should be public, so that the performance of different protection methods may be compared in a fully fair manner. This entry describes general concepts regarding spoofing dataset acquisition and particularizes them to the field of iris recognition. It also gives a summary of the most important features of the public iris spoofing databases currently available.

Introduction

One of the key challenges faced by the rapidly evolving biometric industry is...

This is a preview of subscription content, log in to check access.

References

  1. 1.
    R. Bodade, S. Talbar, Dynamic iris localisation: a novel approach suitable for fake iris detection. Int. J. Comput. Inf. Syst. Ind. Manage. Appl. 2, 163–173 (2010)Google Scholar
  2. 2.
    R. Bodade, S. Talbar, Fake iris detection: a holistic approach. Int. J. Comput. Appl. 19, 1–7 (2011)Google Scholar
  3. 3.
    R. Chen, X. Lin, T. Ding, Liveness detection for iris recognition using multispectral images. Pattern Recognit. Lett. 33, 1513–1519 (2012)CrossRefGoogle Scholar
  4. 4.
    Clarkson University, LivDet-Iris 2013: liveness detection-iris competition (2013), Available online: http://people.clarkson.edu/projects/biosal/iris/
  5. 5.
    J. Galbally, J. Ortiz-Lopez, J. Fierrez, J. Ortega-Garcia, Iris liveness detection based on quality related features, in Proceedings of the International Conference on Biometrics (ICB), New Delhi, 2012, pp. 271–276Google Scholar
  6. 6.
    X. He, Y. Lu, P. Shi, A new fake iris detection method, in Proceedings of the IAPR/IEEE International Conference on Biometrics (ICB), Alghero. LNCS, vol. 5558 (Springer, 2009), pp. 1132–1139Google Scholar
  7. 7.
    A. Lefohn, B. Budge, P. Shirley, R. Caruso, E. Reinhard, An ocularist’s approach to human iris synthesis. IEEE Trans. Comput. Graphics Appl. 23, 70–75 (2003)CrossRefGoogle Scholar
  8. 8.
    T. Matsumoto, Artificial irises: importance of vulnerability analysis, in Proceedings of the Asian Biometrics Workshop (AWB), vol. 45, 2004Google Scholar
  9. 9.
    V. Ruiz-Albacete, P. Tome-Gonzalez, F. Alonso-Fernandez, J. Galbally, J. Fierrez, J. Ortega-Garcia, Direct attacks using fake images in iris verification, in Proceedings of the COST 2101 Workshop on Biometrics and Identity Management (BioID), Roskilde. LNCS, vol. 5372 (Springer, 2008), pp. 181–190Google Scholar
  10. 10.
    L. Thalheim, J. Krissler, Body check: biometric access protection devices and their programs put to the test, c’t Magazine, Nov 2002, pp. 114–121Google Scholar
  11. 11.
    U.C. von Seelen, Countermeasures against iris spoofing with contact lenses, in Proceedings of the Biometrics Consortium Conference, Arlington, Virginia, 2005Google Scholar
  12. 12.
    Z. Wei, X. Qiu, Z. Sun, T. Tan, Counterfeit iris detection based on texture analysis, in Proceedings of the IEEE International Conference on Pattern Recognition (ICPR), Tampa, 2008Google Scholar
  13. 13.
    H. Zhang, Z. Sun, T. Tan, Contact lens detection based on weighted LBP, in Proceedings of the IEEE International Conference on Pattern Recognition (ICPR), Istanbul, 2010, pp. 4279–4282Google Scholar
  14. 14.
    H. Zhang, Z. Sun, T. Tan, J. Wang, Learning hierarchical visual codebook for iris liveness detection, in International Joint Conference on Biometrics, Washington DC, 2011Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Joint Research Centre, European CommissionIspraItaly