针对基于深度学习的虹膜分割模型存在参数量大、计算量大、占用空间大的问题，提出了一种轻量级的虹膜分割模型。首先，将Linknet中特征提取网络替换为改进的轻量级网络MobileNetv3。这种设计在保持准确性的同时显著地提高了模型效率。其次，为了减少虹膜特征信息丢失，设计了一个多尺度特征提取模块。再次，引入了通道注意力机制，抑制无关噪声，加大虹膜区域的权重。最后，在3个虹膜数据库上将本文模型与其他虹膜分割模型进行比较，结果表明，本文模型在虹膜分割准确率和效率之间取得了更好的平衡。

Aiming at the problem that deep learning-based iris segmentation models need a large number of parameters， computation cost， and space occupation， a lightweight iris segmentation model is proposed in this paper. First， the feature extraction network of Linknet is replaced with the improved lightweight deep neural network MobileNetv3. This design significantly improves the efficiency of the model while maintaining accuracy. Then， in order to reduce the loss of iris feature information， a multiscale feature extraction module is designed in this paper. Once again， an efficient parallel attention mechanism is introduced to suppress noise interference and enhance the weight of iris region pixels. Finally， the proposed model was compared with other iris segmentation models on three iris databases， and the results showed that the model achieved a better balance between iris segmentation accuracy and efficiency.