<p>Visible-infrared cross-modality pedestrian re-identification (Cm-ReID) devotes to mapping the pedestrian photographs of the same identity from different cameras. Despite significant achievements in previous CNN-based works, there remain two limitations: (i) cross-modality feature mismatch caused by data heterogeneity and (ii) intra-modality feature variations resulting from different human postures and shooting angles. To overcome these limitations, in this paper, we proposed a Cm-ReID model based on dual-constraint capsule network (DCCN) to explore feature representations among modalities. Specifically, to cope with cross-modality feature mismatch, we design a modality mitigation module (MMM). It utilizes channel attention mechanism, extracts discriminative features from feature maps to better mitigate the modality discrepancy. With channel attention mechanism, the DCCN can better perceive identity information on both modalities. Furthermore, to address intra-modality feature disparity, we design dual-constraint mechanism in DCCN, which consists of intra-class aggregation module (CAM) and inter-class sparse module (ISM). The former is proposed to aggregate the features of the same pedestrian under different perspectives, and the latter is to sparse the features of different pedestrians. Comprehensive experiments on the public SYSU-MM01 and RegDB datasets reveal the DCCN’s superiority over current practices.</p>