【Objective】Visible-infrared person re-identification is a very challenging image retrieval problem due to the huge modal difference between visible and infrared images.【Method】In order to further reduce the difference between the two modalities and focus on pedestrian information, a network structure based on dual attention mechanism is proposed for visible-infrared person re-identification. On the one hand, through the dual attention mechanism to mine personal spatial information of different scales and enhance the channel interaction ability of local features. On the other hand, through learning multi-granular feature information through using global and local branches, different granular information can complement with each other to form a more discriminating feature.【Result】Experimental results on two public datasets show that the proposed method has a significant improvement compared with the baseline, and shows ideal performance on both the RegDB dataset and the SYSU-MM01 dataset.【Conclusion】The proposed method can provide an effective reference for solving the problem of modal difference of visible-infrared person re-identification in the future.