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Fast discrete curvelet transform based anisotropic iris coding and recognition using k-out-of-n: A fused post-classifier

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Abstract

This paper presents rotation invariant technique for iris feature extraction and fused post-classification at the decision level to improve the performance under non-ideal environmental conditions. In this work, directional iris texture features based on two-dimensional (2D) Fast Discrete Curvelet Transform (FDCT) are computed. This approach divides the normalized iris image into six sub-images. The curvelet transform is applied on each sub-image. The feature vector for each sub-image is derived using the directional energies of these curvelet coefficients. These distances are fused at the decision level through novel post-classifier using k-out-of-n: A scheme to reduce the false rejection rate. The feasibility of the proposed algorithm has been tested using UBIRIS, MMU1 and CASIA-Iris V2.0 databases and performance is compared with some of the well-known existing iris recognition algorithms. The experimental results show that the performance is comparable with some of the state-of-the-art iris recognition algorithms.

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Authors and Affiliations

  1. Department of Instrumentation and Control Engineering, A.I.S.S.M.S’ Institute of Information Technology, Pune, Maharashtra, India

    Amol D. Rahulkar

  2. Department of Electronics Engineering, Bhivarabai Sawant College of Engineering and Research, Pune, Maharashtra, India

    Dattatray V. Jadhav

  3. Department of Instrumentation Engineering, S.G.G.S. Institute of Engineering and Technology, Vishnupuri, Nanded, Maharashtra, India

    Raghunath S. Holambe

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  1. Amol D. Rahulkar
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  2. Dattatray V. Jadhav
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  3. Raghunath S. Holambe
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Correspondence to Amol D. Rahulkar.

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Rahulkar, A.D., Jadhav, D.V. & Holambe, R.S. Fast discrete curvelet transform based anisotropic iris coding and recognition using k-out-of-n: A fused post-classifier. Machine Vision and Applications 23, 1115–1127 (2012). https://doi.org/10.1007/s00138-011-0370-8

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  • DOI: https://doi.org/10.1007/s00138-011-0370-8

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