A Model Based, Anatomy Based Method for Synthesizing Iris Images

  • Jinyu Zuo
  • Natalia A. Schmid
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


Popularity of iris biometric grew considerably over the past 2-3 years. It resulted in development of a large number of new iris encoding and processing algorithms. Since there are no publicly available large scale and even medium size databases, neither of the algorithms has undergone extensive testing. With the lack of data, two major solutions to the problem of algorithm testing are possible: (i) physically collecting a large number of iris images or (ii) synthetically generating a large scale database of iris images. In this work, we describe a model based/anatomy based method to synthesize iris images and evaluate the performance of synthetic irises by using a traditional Gabor filter based system and by comparing local independent components extracted from synthetic iris images with those from real iris images. The issue of security and privacy is another argument in favor of generation of synthetic data.


Independent Component Analysis Independent Component Analysis Synthetic Dataset Iris Image Minimum Euclidean Distance 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jinyu Zuo
    • 1
  • Natalia A. Schmid
    • 1
  1. 1.Lane Department of Computer Science and Electrical EngineeringWest Virginia UniversityMorgantownUSA

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