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Texture code matrix-based multi-instance iris recognition

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Abstract

This paper proposes a novel texture feature for iris recognition. The iris recognition system consists of three major components: pre-processing, feature extraction and classification. During pre-processing, iris is segmented using constrained circular Hough transform, which reduces both time and space complexity. In this work, from normalized iris image, a novel texture code matrix is generated, which is then used to obtain a co-occurrence matrix. Finally, desired texture features are computed from this co-occurrence matrix. Here, a two-class classification technique is adopted to develop a multi-class multimodal biometric system using fusion. The performance of the proposed system is tested on four standard iris image databases, namely UPOL, CASIA-Iris V3 Interval, MMU1 and IITD, which shows the efficacy of the proposed feature.

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Authors and Affiliations

  1. Electronics and Communication Sciences Unit, Indian Statistical Institute, B.T. Road, Kolkata, 700108, West Bengal, India

    Saiyed Umer & Bhabatosh Chanda

  2. Department of Information Technology, Jadavpur University, Kolkata, India

    Bibhas Chandra Dhara

Authors
  1. Saiyed Umer
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  2. Bibhas Chandra Dhara
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  3. Bhabatosh Chanda
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Correspondence to Saiyed Umer.

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Umer, S., Dhara, B.C. & Chanda, B. Texture code matrix-based multi-instance iris recognition. Pattern Anal Applic 19, 283–295 (2016). https://doi.org/10.1007/s10044-015-0482-2

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