In visible-infrared cross-modal person re-identification， the modal differences will lead to low identification accuracy. Therefore， a dual-stream structure based cross-modal person re-identification relation network， named IVRNBDS （Infrared and Visible Relation Network Based on Dual-stream Structure）， was proposed. Firstly， the dual-stream structure was used to extract the features of the visible light modal and the infrared modal person images respectively. Then， the feature map of the person image was divided into six segments horizontally to extract relationships between the local features of each segment and the features of other segments of the person and the relationship between the core features and average features of the person. Finally， when designing loss function， the Hetero-Center triplet Loss （HC Loss） function was introduced to relax the strict constraints of the ordinary triplet loss function， so that image features of different modals were able to be better mapped into the same feature space. Experimental results on public datasets SYSU-MM01 （SunYat-Sen University MultiModal re-identification） and RegDB （Dongguk Body-based person Recognition） show that the computational cost of IVRNBDS is slightly higher than those of the mainstream cross-modal person re-identification algorithms， but the proposed network has the Rank-1 （similarity Rank 1） and mAP （mean Average Precision） improved compared to the mainstream algorithms， increasing the recognition accuracy of the cross-modal people re-identification algorithm.
