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Smartphone based iris recognition through optimized textural representation

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Abstract

Mobile devices have become ubiquitous nowadays and so is the need of secure access to these devices. Iris being the most reliable and hard-to-tamper biometric trait, can serve the aforementioned purpose. Iris recognition on mobile phones has become a significant and challenging task for the research community. With advancement in technology, it has now become feasible to use mobile devices’ in-built cameras to unlock the device through the user’s iris. This paper presents a convenient and efficient approach: optimal bit-transition codes (OBTC), for representing mobile iris images in a more distinctive manner. The approach is derived from the texture analysis property of 2D Gabor filters. Optimization of Gabor parameters is performed for iris images from two challenging mobile iris databases: MICHE I (which comprises of eye images acquired from three different smartphones: iPhone5, Galaxy S4 and Galaxy Tab2) and VISOB (which contains eye images acquired from iPhone5S, Samsung Note 4 and Oppo N1). After filtering, the image responses are converted to binary numbers and stored in concatenated vectors. Later, the concatenated vectors produce binary strings across the direction of concatenation and number of bit-transitions in these binary strings are encoded to form the complete feature vectors. A capacious experimentation is performed on the challenging MICHE I and VISOB iris databases. Comparison of the proposed approach with several state-of-the-art approaches clearly shows its expediency. More importantly, the proposed iris recognition approach performs at par with a commercial iris matcher, named VeriEye, which proves its usefulness.

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Acknowledgements

The authors would like to thank Biometric and Image Processing Lab (BIPLab) at University of Salerno, Fisciano, Italy and Computational Intelligence and Bio-Identification Technologies Lab (CIBIT), University of Missouri-Kansas City, for providing access to their mobile iris databases.

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Authors and Affiliations

  1. Bennett University, Greater Noida, UP, India

    Ritesh Vyas

  2. National Institute of Technology Delhi, Delhi, 110040, India

    Tirupathiraju Kanumuri, Gyanendra Sheoran & Pawan Dubey

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  1. Ritesh Vyas
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  2. Tirupathiraju Kanumuri
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  3. Gyanendra Sheoran
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  4. Pawan Dubey
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Correspondence to Ritesh Vyas.

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Vyas, R., Kanumuri, T., Sheoran, G. et al. Smartphone based iris recognition through optimized textural representation. Multimed Tools Appl 79, 14127–14146 (2020). https://doi.org/10.1007/s11042-019-08598-7

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  • DOI: https://doi.org/10.1007/s11042-019-08598-7

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