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Robust Iris Recognition Using Moment Invariants

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Abstract

Iris recognition under less constrained environment poses a challenge to be considered for high-security applications. In this paper, discrete orthogonal moment-based features including Tchebichef, Krawtchouk and Dual-Hahn are proposed which prove to be effective for both near-infrared and visible images. The local as well as global features are extracted from localized iris regions till 15th order with invariance (scale, rotation, translation and illumination) properties and tolerance to noise. The performance of the moment-based features is evaluated on four publicly available databases: CASIA-IrisV4-Interval, IITD.v1, UPOL and UBIRIS.v2. It is found that the proposed method gives encouraging results in terms of accuracy, equal error rate and decidability index as compared to the competing techniques available in the literature.

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Acknowledgements

The authors acknowledge Chinese Academy of Sciences-Institute of Automation (CASIA), China, Indian Institute of Technology, Delhi (IITD), University of Palack´eho and Olomouc and Soft Computing and Image Analysis Group (SOCIA Lab.), Department of Computer Science, University of Beira Interior, Covilhã, Portugal for providing iris databases used in this work. The authors are also grateful to anonymous reviewers for their constructive comments which improved the quality of the manuscript.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, India

    Bineet Kaur & Sukhwinder Singh

  2. Department of Electrical Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, India

    Jagdish Kumar

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  1. Bineet Kaur
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  2. Sukhwinder Singh
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  3. Jagdish Kumar
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Correspondence to Bineet Kaur.

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Kaur, B., Singh, S. & Kumar, J. Robust Iris Recognition Using Moment Invariants. Wireless Pers Commun 99, 799–828 (2018). https://doi.org/10.1007/s11277-017-5153-8

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