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

Jing Huang, Xinge You*, Yuan Yuan, Feng Yang, Lin Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)883-894
Number of pages12
Issue number4-6
Early online date18 Nov 2009
Publication statusPublished - Jan 2010

Bibliographical note

Bayesian Networks / Design and Application of Neural Networks and Intelligent Learning Systems (KES 2008 / Bio-inspired Computing: Theories and Applications (BIC-TA 2007)


  • Iris recognition
  • Kernel Fisher classifiers
  • Markov random fields
  • Non-separable wavelet transform


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