In this paper an adaptive iris segmentation algorithm is presented. In the proposed algorithm Otsu Threshold value, average gray level of the image, image size, Hough-Circle search are used for adaptive segmentation of irises. Otsu threshold is used for selecting threshold value in order to determine pupil location. Then Hough circle is utilized for pupillary boundary, and finally gradient search is used for the limbic boundary detection. The algorithm achieved 98% segmentation rate in batch processing of the CASIA version 1 (756 Images) and version 3 (CASIA-IrisV3-Interval, 2655 Images) databases.


Independent Component Analysis Iris Image Iris Recognition Iris Boundary Average Gray Level 
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 2009

Authors and Affiliations

  • Rahib Abiyev
    • 1
  • Kemal Kilic
    • 1
  1. 1.Dept. of Computer EngineeringNear East UniversityNicosiaCyprus

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