A Low-quality Iris Image Segmentation Algorithm Based on SRN-UNet

In recent years, iris recognition has been widely used in various fields. Iris segmentation is the most critical step in the iris recognition process. The accuracy of the iris segmentation algorithm directly affects the performance of the entire iris recognition system.In this study, an iris image segmentation algorithm SRN-UNet (SeResNext-UNet) is proposed to solve the problem of low segmentation accuracy for segmenting low-quality iris images. In the coding stage, the SE-ResNext module is added, which is cascaded with the SENet (Squeeze-and-Excitation Network) module after the RexNext module. The ResNext module can improve the network performance without increasing the network parameters; the SENet module builds a network model from the perspective of feature channel correlation through squeeze, excitation, and weight redistribution. For low-quality iris images, the SENet uses global information to selectively emphasize informative features and suppress less useful ones, and improve the accuracy of iris segmentation. In the up-sampling layer of the decoding stage, the amount of model parameters is reduced to increase the training speed. In order to solve the problem of image category imbalance, the SRN-UNet is trained by combining the Focal loss function and the Dice loss function. Among them, the Focal loss function can reduce the weight of easy-to-classify samples, make the model pay more attention to the training of difficult samples, and guide the network to retain complex boundary details; the Dice loss function can solve the problem of pixel category imbalance and alleviate the noise caused by the Focal loss function. Experimental results based on CASIA-Iris dataset and self-built low-quality iris image dataset show that compared with other algorithms, the proposed algorithm has better segmentation effects in terms of visual effects and objective evaluation indicators. Among them, the Mean Intersection Over Union of the proposed algorithm reached 95.19%, the F1 score reached 97.48%, and the Precision reached 97.82%. Compared with U-Net, the Mean Intersection Over Union, F1 score and Precision of proposed algorithm have increased by 4.20%, 2.27%, and 5.38% respectively, and the algorithm is faster than U-Net.