Abstract
This paper presents a novel iris feature extraction scheme using two-dimensional non-separable, non-redundant, multi-scale hybrid finer directional wavelet filter bank and classification using fused post-classifier under non-ideal environmental conditions. The proposed approach overcomes limited directionality of wavelet transform. The designed filter bank converts the wavelet basis functions to a set of directional basis elements by employing the combination of designed bi-orthogonal wavelet filter bank and modified checkerboard-shaped filter bank (designed using triplet of half-band filters). The false rejection rate is reduced with the help of k-out-of-n:A rule. The proposed technique is capable to handle possible artifacts especially inaccurate iris localization, occlusion of eyelids, pupil, eyelashes, reflection on iris, non-linear deformation, head-tilt, etc. The performance of the proposed scheme is validated using CASIA-Iris V2.0, MMU1, and UBIRIS databases. Finally, the performance of proposed approach is compared with other existing iris recognition algorithms to demonstrate its potential.
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Acknowledgments
The authors are very much thankful to the editor, associate editor, and anonymous reviewers for their constructive comments and valuable suggestions that helped to improve the quality of this manuscript significantly. The authors would like to thank Chinese Academy of Sciences’ Institute of Automation (CASIA), U.B.I (Portugal), and MMU1 for providing the iris databases used in this paper.
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Rahulkar, A.D., Waghmare, L.M. & Holambe, R.S. A new approach to the design of hybrid finer directional wavelet filter bank for iris feature extraction and classification using k-out-of-n:A post-classifier. Pattern Anal Applic 17, 529–547 (2014). https://doi.org/10.1007/s10044-013-0334-x
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DOI: https://doi.org/10.1007/s10044-013-0334-x