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A light iris segmentation network

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Abstract

Iris segmentation plays a vital role in the iris recognition system. However, it faces many challenges in non-ideal situations. To improve the iris segmentation performance for possible mobile devices, this paper presents a light iris segmentation method based on fully convolutional network. Firstly, a lightweight fully convolutional iris segmentation network is developed. Secondly, we adopt weighted loss, multi-level feature dense fusion module, multi-supervised training of multi-scale image and generative adversarial network to improve the segmentation performance. The final model is 6.21 M. Experiments show that the proposed method achieves 99.30% PA, 95.35% mIoU on UBIRIS.v2 and 99.66% PA, 96.75% mIoU on CASIA-Iris-Thousand database, which is relatively encouraging for a light iris segmentation network. It takes 41.56 ms and 63.03 ms to segment an image of UBIRIS.v2 and CASIA-Iris-Thousand databases, respectively.

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Acknowledgements

The authors would like to thank “National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)” for their great contributions in building and providing iris image databases.

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Authors and Affiliations

  1. Department of Mathematics, College of Sciences, Northeastern University, Shenyang, 110819, China

    Qi Wang, Xiangyue Meng, Ting Sun & Xiangde Zhang

  2. Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang, China

    Qi Wang

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  1. Qi Wang
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  2. Xiangyue Meng
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Correspondence to Xiangde Zhang.

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This work is supported by National Natural Science Foundations of China, No. 61703088, the Doctoral Scientific Research Foundation of Liaoning Province, No.20170520326 and “the Fundamental Research Funds for the Central Universities”, N2105009.

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Wang, Q., Meng, X., Sun, T. et al. A light iris segmentation network. Vis Comput 38, 2591–2601 (2022). https://doi.org/10.1007/s00371-021-02134-1

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