Encyclopedia of Biometrics

2009 Edition
| Editors: Stan Z. Li, Anil Jain

Iris Databases

  • Damon L. Woodard
  • Karl Ricanek
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-73003-5_168


An iris database is a collection of images that contain, at a minimum, the iris region of the eye. The images are typically collected by sensors that operate in the  visible spectrum, 380–750 nm, or the near infrared spectrum (NIR), 700–900 nm. The visible spectrum image can be stored as a color image or as an intensity image. The NIR image is always stored as an intensity image.


Successful biometric research requires the analysis of human data. For biometric researchers to demonstrate the effectiveness of proposed iris segmentation/recognition techniques and allow fair comparisons with existing methods, publicly available iris databases are required. The perfect iris-image database should be sufficiently large, consist of images collected from a large and heterogeneous group of subjects, and contain images that depict noise factors typically encountered in real world applications. In the following sections, several publicly and freely available iris-image...

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  1. 1.
    Institute of Automation, Chinese Academy of Science: CASIA v1.0 Iris Image Database, 2008. http://www.nlpr.ia.ac.cn/english/irds/irisdatabase.htm. Accessed 27 Dec, 2008
  2. 2.
    Phillips, P.J., Bowyer, K.W., Flynn, P.J.: Comment on the CASIA version 1.0 Iris Dataset, IEEE Trans. Pattern Anal. Mach. Intell. 29(10), 1869-1870 (2007)CrossRefGoogle Scholar
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    Institute of Automation, Chinese Academy of Science: CASIA v3.0 Iris Image Database, 2008. http://www.nlpr.ia.ac.cn/english/irds/irisdatabase.htm. Accessed 27 Dec, 2008
  4. 4.
    Dobeš, M., Machala, L.: UPOL Iris Image Database, 2008. http://phoenix.inf.upol.cz/iris/. Accessed 27 Dec, 2008
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    Dobeš, M., Machala, L., Tichavský, P., Pospíšil J.: Human Eye Iris Recognition Using the Mutual Information. Optik 115(9), 399–405 (2004)Google Scholar
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    University of Bath: University of Bath Iris Image Database, 2008. http://www.bath.ac.uk/eleceng/research/sipg/irisweb/index.html. Accessed 27 Dec, 2008
  7. 7.
    National Institute of Standards and Technology: Iris Challenge Evaluation (ICE), 2008. http://iris.nist.gov/ICE/. Accessed 27 Dec, 2008
  8. 8.
    Liu, X., Bowyer, K.W., Flynn, P.J.: Iris Recognition and Verification Experiments with Improved Segmentation Method. In Proceedings of Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID). Buffalo, NY, 17–18 October 2005Google Scholar
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    Multimedia University: MMU1 and MMU2 Iris Image Databases, 2008. http://pesona.mmu.edu.my/~ccteo. Accessed 27 Dec, 2008
  10. 10.
    West Virginia University: West Virginia University Biometric Dataset Collections, 2008. http://www.csee.wvu.edu/~simonac/CITeR_DB. Accessed 27 Dec, 2008
  11. 11.
    Ross, A., Crihalmeanu, S., Hornak, L., Schuckers, S.: A Centralized Web-Enabled Multimodal Biometric Database. In Proceedings of the 2004 Biometric Consortium Conference (BCC), Arlington, VA, September 2004Google Scholar
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    Proença, H., Alexandre, L.: UBIRIS: A Noisy Iris Image Database. In: Proceedings of the 13th International Conference on Image Analysis and Processing (ICIA2005), Vol. 1, pp. 970–977, 2005Google Scholar
  13. 13.
    SOCIA Lab – University of Beira Interior: UBIRIS.v1 Iris Image Database, 2008. http://iris.di.ubi.pt/ubiris1.html. Accessed 27 Dec, 2008
  14. 14.
    SOCIA Lab – University of Beira Interior: Noisy Iris Challenge Evaluation – Part I, 2008. http://nice1.di.ubi.pt/

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Damon L. Woodard
    • 1
  • Karl Ricanek
    • 2
  1. 1.Clemson UniversityClemsonUSA
  2. 2.University of North Carolina WilmingtonWilmingtonUSA