Iris Segmentation for Challenging Periocular Images

  • Raghavender JillelaEmail author
  • Arun A. Ross
  • Vishnu Naresh Boddeti
  • B. V. K. Vijaya Kumar
  • Xiaofei Hu
  • Robert Plemmons
  • Paúl Pauca
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


This chapter discusses the performance of five different iris segmentation algorithms on challenging periocular images. The goal is to convey some of the difficulties in localizing the iris structure in images of the eye characterized by variations in illumination, eyelid and eyelash occlusion, defocus blur, motion blur, and low resolution. The five algorithms considered in this regard are based on the (a) integrodifferential operator, (b) Hough transform, (c) geodesic active contours, (d) active contours without edges, and (e) directional ray detection method. Experiments on the Face and Ocular Challenge Series (FOCS) database highlight the pros and cons of the individual segmentation algorithms.


Active Contour Iris Image Iris Recognition Illumination Normalization Iris Boundary 
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.



This work was sponsored under IARPA BAA 09-02 through the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-10-2- 0013. The views and conclusions contained in this chapter are those of the authors and should not be interpreted as representing of official policies, either expressed or implied, of IARPA, the Army Research Laboratory, or the US government. The US government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation herein.


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Raghavender Jillela
    • 1
    Email author
  • Arun A. Ross
    • 1
  • Vishnu Naresh Boddeti
    • 2
  • B. V. K. Vijaya Kumar
    • 2
  • Xiaofei Hu
    • 3
  • Robert Plemmons
    • 3
  • Paúl Pauca
    • 3
  1. 1.West Virginia UniversityMorgantownUSA
  2. 2.Carnegie Mellon UniversityPittsburghUSA
  3. 3.Wake Forest UniversityWinston-SalemUSA

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