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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