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Structure correlation-aware attention for Iris recognition

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Abstract

Formation of a human iris is mainly determined by anatomical characteristics that are identity-related and stable for personal recognition. However, existing deep Convolutional Neural Network (ConvNet)-based iris recognition methods fail to make fully use of such anatomical structure. To address this issue, we propose a dual attention block, dubbed unified global radial and angular attention (UGRAA), which focuses on both global radial and angular correlations of iris texture by aggregating the global contexts. The UGRAA is a lightweight and architecture-agnostic module, suitable for commonly used ConvNets in an end-to-end fashion. Furthermore, for better characterizing spatial relationships between iris regions, we introduce pair-wise spatial correlations via second-order pooling. We conduct extensive experiments to evaluate the proposed network, which is called UGRAA-Net, on four challenging iris recognition benchmarks. The results verify that our UGRAA-Net consistently outperforms its counterparts, achieving state-of-the-art (SOTA) performance.

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Data availability

The datasets analyzed during the current study are available from (https://cvrl.nd.edu/projects/data/#nd-iris-0405-data-set) and (http://biometrics.idealtest.org/), owned by UND Principal Investigator (http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.59) and Professor Tieniu Tan, respectively, but restrictions apply to the availability of these data, which are used under license from UND Principal Investigator and Professor Tieniu Tan, respectively, and so are not publicly available. Data are, however, available from the authors upon reasonable request and permission of UND Principal Investigator and Professor Tieniu Tan, respectively.

Notes

  1. The logspace refers to the MATLAB-style function.

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Acknowledgements

This research is supported by National Natural Science Foundation of China under grant no. 61971086 and 61471082.

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  1. School of Information and Communication Engineering, Dalian University of Technology (DUT), Dalian, 116024, China

    Lingyao Jia, Qiule Sun & Peihua Li

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  1. Lingyao Jia
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Correspondence to Peihua Li.

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Jia, L., Sun, Q. & Li, P. Structure correlation-aware attention for Iris recognition. Neural Comput & Applic 35, 21071–21091 (2023). https://doi.org/10.1007/s00521-023-08800-w

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