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Iris recognition with tunable filter bank based feature

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Abstract

In this paper, a tunable filter bank is proposed to extract region based features from non-cooperative iris images. The proposed method is based on half band polynomial of 14th order. The existing iris recognition algorithms work well with highly good quality images acquired under constraint and cooperative environments. These well-known techniques fail to perform well, when the raw images encounter segmentation failure during preprocessing. Their accuracy falls drastically, especially when the iris images are highly occluded due to different artifacts. Apart from that, segmentation failure during iris normalization makes the feature extraction more difficult. In our work, a tunable biorthogonal filter bank is proposed, for which the filter co-efficients are extracted from polynomial domain instead of z-domain. The proposed filter bank provides an opportunity to tuning and optimize the filter co-efficients. Experimental results using publicly available databases like CASIAv3, UBIRISv1, and IITD show the superiority of the proposed feature over the existing ones given its low template size, low feature extraction and feature matching time. Though accuracy yielded by the proposed filter bank is of the same order as found with existing features, achieving similar accuracy as state-of-the-art methods with less time and computation is a substantial development due to real-time usage of biometric systems.

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Acknowledgment

The research presented in this article is funded by the following grants:

1. Grant no. 12(5)/2012-ESD by Department of Electronics and Information Technology, Government of India.

2. Grant no. ETI/359/2014 by Department of Science and Technology, Government of India under the Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions (FIST) Program 2016.

The research presented in this article is an extension to the work presented in the following article: Barpanda SS, Majhi B, Sa PK (2015) Region-based feature extraction from non-cooperative iris images using CDF 9/7 filter bank. Innov Syst Softw Eng 11(3):197–202. doi:10.1007/s11334-015-0251-9.

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  1. School of Computer Science & Engineering, VIT University, Amaravathi, Guntur, Andhra Pradesh, 522 034, India

    Soubhagya Sankar Barpanda

  2. Department of Computer Science & Engineering, National Institute of Technology, Rourkela, Odisha, 769 008, India

    Pankaj K. Sa, Banshidhar Majhi & Sambit Bakshi

  3. Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA

    Oge Marques

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  1. Soubhagya Sankar Barpanda
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Correspondence to Sambit Bakshi.

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Barpanda, S.S., Sa, P.K., Marques, O. et al. Iris recognition with tunable filter bank based feature. Multimed Tools Appl 77, 7637–7674 (2018). https://doi.org/10.1007/s11042-017-4668-z

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