西安电子科技大学学报（自然科学版）2025，Vol.52Issue(5)：88-98,11.DOI:10.19665/j.issn1001-2400.20250806
一种协同注意力机制虹膜分割算法
Collaborative attention mechanism-based iris segmentation algorithm
摘要
Abstract
Aiming at the problem of poor performance of iris segmentation for low-quality images,this paper proposes an iris segmentation algorithm based on the collaborative attention mechanism.Based on the U-Net model under the deep learning framework,this algorithm innovatively introduces a dual attention module of the regional attention mechanism and the quality-aware attention mechanism,and collaboratively improves the accuracy of iris region segmentation through the two dimensions of position perception and image quality perception.Specifically,the regional attention mechanism predicts the area where the iris ring is located and constrains the target spatial area during the feature extraction process,thereby effectively reducing background noise interference.The quality-aware attention mechanism dynamically adjusts the focus on key features in the convolutional attention module based on the image quality assessment results,thereby significantly enhancing the key feature expression ability of low-quality images.Experimental results on public datasets and self-made datasets show that this algorithm outperforms many mainstream segmentation models such as U-Net and IrisParseNet in the two core segmentation evaluation metrics of intersection and union ratio and accuracy rate.Especially under the conditions of low-quality images such as low illumination and motion blur,the improvement of segmentation effect is more significant.These improvements provide reliable technical support for the practical application of iris recognition systems in complex environments.关键词
图像分割/深度学习/注意力机制/区域约束/图像质量感知Key words
image segmentation/deep learning/attention mechanism/regional constraint/image quality perception分类
信息技术与安全科学引用本文复制引用
李永博,杜建超,王军宁,张铭津..一种协同注意力机制虹膜分割算法[J].西安电子科技大学学报（自然科学版）,2025,52(5):88-98,11.基金项目
国家自然科学基金(62272363,92470108) （62272363,92470108）
