Rotation invariant iris feature extraction using Gaussian Markov random fields with non-separable wavelet

  • Authors:

  • Jing Huang;Xinge You;Yuan Yuan;Feng Yang;Lin Lin

  • Affiliations:

  • Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China and School of Biomedical Engineering, Southern Medical University, Guangzh ...;Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;School of Engineering and Applied Science, Aston University, Birmingham B4 7ET, UK;School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China;School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China

  • Venue:
  • Neurocomputing
  • Year:
  • 2010

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Abstract

Rotation invariance is important for an iris recognition system since changes of head orientation and binocular vergence may cause eye rotation. The conventional methods of iris recognition cannot achieve true rotation invariance. They only achieve approximate rotation invariance by rotating the feature vector before matching or unwrapping the iris ring at different initial angles. In these methods, the complexity of the method is increased, and when the rotation scale is beyond the certain scope, the error rates of these methods may substantially increase. In order to solve this problem, a new rotation invariant approach for iris feature extraction based on the non-separable wavelet is proposed in this paper. Firstly, a bank of non-separable orthogonal wavelet filters is used to capture characteristics of the iris. Secondly, a method of Markov random fields is used to capture rotation invariant iris feature. Finally, two-class kernel Fisher classifiers are adopted for classification. Experimental results on public iris databases show that the proposed approach has a low error rate and achieves true rotation invariance.