DualSANet: Dual Spatial Attention Network for Iris Recognition

Kai Yang, Zihao Xu, Jingjing Fei; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 889-897

Compared with other human biosignatures, iris has more advantages on accuracy, invariability and robustness. However, the performance of existing common iris recognition algorithms is still far from expectations of the community. Although some researchers have attempted to utilize deep learning methods which are superior to traditional methods, it is worth exploring better CNN network architecture. In this paper, we propose a novel network architecture based on the dual spatial attention mechanism for iris recognition, called DualSANet. Specifically, the proposed architecture can generate multi-level spatially corresponding feature representations via an encoder-decoder structure. In the meantime, we also propose a new spatial attention feature fusion module, so as to ensemble these features more effectively. Based on these, our architecture can generate dual feature representations which have complementary discriminative information. Extensive experiments are conducted on CASIA-IrisV4-Thousand, CASIA-IrisV4-Distance, and IITD datasets. The experimental results show that our method achieves superior performance compared with the state-of-the-arts.