High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling intra-class variation for nonideal iris recognition
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
A non-linear normalization model for iris recognition
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Pupil dilation degrades iris biometric performance
Computer Vision and Image Understanding
A New Fake Iris Detection Method
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
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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.