Nonlinear Iris Deformation Correction Based on Gaussian Model

  • Zhuoshi Wei
  • Tieniu Tan
  • Zhenan Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


Current iris recognition systems can achieve high level of success under restricted conditions, while they still face challenges of utilizing images with heavy deformation caused by illumination variations. Developing methods to alleviate the deformation becomes a necessity, since the requirement of uniform lighting is often not practical. This paper introduces a novel algorithm to counteract elastic iris deformation. In the proposed algorithm, for nonlinear iris stretch, the distance of any point in the iris region to the pupil boundary is assumed to be the corresponding distance under linear stretch plus an additive deviation. Gaussian function is employed to model the deviation. Experimental results on two databases with nonlinear deformation demonstrate the effectiveness of the algorithm. The proposed iris deformation correction algorithm achieves a lower Equal Error Rate (EER), compared to the other two linear and nonlinear normalization methods in the literature, making the system more robust in realistic environments.


Iris Image Pupil Size Equal Error Rate Additive Deviation False Reject Rate 
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 2007

Authors and Affiliations

  • Zhuoshi Wei
    • 1
  • Tieniu Tan
    • 1
  • Zhenan Sun
    • 1
  1. 1.Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. P.O. Box 2728, Beijing, 100080P.R. China

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