Encyclopedia of Biometrics

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

Iris Sample Synthesis

  • Natalia A. Schmid
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-73003-5_5


Synthetic iris images


Iris image synthesis is a process of creating images of an iris by means of statistical and stochastic models, computer graphic tools or through manipulating, modifying or transforming parts or complete images collected from real irises. Since the size of synthesized datasets can be made arbitrary large, these data are suggested to be used for the purpose of extensive testing of the performance and efficiency of newly designed iris recognition algorithms.


Iris as a biometric has been known for a long time. However, only in the recent years it has gained a substantial attention of both the research community and governmental organizations resulting in the development of a large number of new iris encoding and processing algorithms. Most of the designed systems and algorithms are claimed to have exclusively high recognition performance. However, since there are no publicly available large-scale and even medium-size datasets, only very...
This is a preview of subscription content, log in to check access


  1. 1.
    Cui, J., Wang, Y., Huang, J., Tan, T., Sun, Z.: An iris image synthesis method based on pca and super-resolution. In: Proceedings of 17th International Conference on Pattern Recognition (ICPR’04), Vol. 4, pp. 471–474.Cambridge, UK (2004)Google Scholar
  2. 2.
    Zuo, J., Schmid, N.A., Chen, X.: On generation and analysis of synthetic iris images. IEEE Trans. Inform. Forensics Security, 2(1), 77–90 (2007)CrossRefGoogle Scholar
  3. 3.
    Lefohn, A., Budge, B., Shirley, P., Caruso, R., Reinhard, E.: An ocularist’s approach to human iris synthesis. IEEE Comput. Graph. Appl. 23(6), 70–75 (2003)CrossRefGoogle Scholar
  4. 4.
    Makthal, S., Ross, A.: Synthesis of iris images using markov random fields. In: Proceedings of 13th European Signal Processing Conference (EUSIPCO’05). Antalya, Turkey (2005)Google Scholar
  5. 5.
    Mansfield, A.J., Wayman, J.L.: Best practices in testing and reporting performance of biometric devices. http://www.cesg.gov.uk/site/ast/biometrics/media/BestPractice.pdf
  6. 6.
    Shah, S., Ross, A.: Generating synthetic irises by feature agglomeration. In: Proceedings of the International Conference Image Processing. Atlanta, GA (2006)Google Scholar
  7. 7.
    Wecker, L., Samavati, F., Gavrilova, M.: A reverse subdivision application. In: Proceedings of International Conference on Computer Graphics and Interactive Techniques in Australasia and Southeast Asia (GRAPHITE’05), pp. 121–125. Dunedin, New Zealand (2005)CrossRefGoogle Scholar
  8. 8.
    Zuo, J., Schmid, N.A.: A model based, anatomy based method for synthesizing iris images. In: International Conference on Biometrics (ICB’2006). pp. 428–435. Hong Kong, China (2006)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Natalia A. Schmid

There are no affiliations available