Bridging modal gaps: A Cross-Modal Feature Complementation and Feature Projection Network for visible-infrared person re-identification

Visible-infrared person re-identification (VI-ReID) presents a significant challenge due to the substantial modal differences between infrared (IR) and visible (VIS) images, primarily resulting from their distinct color distributions and textural characteristics. One effective strategy for reducing this modal gap is to utilize feature projection to create a shared embedded space for the modal features. However, a key research question remains: how to effectively align cross-modal features during projection while minimizing the loss of information. To address this challenge, this paper proposed a Cross-Modal Feature Complementation and Feature Projection Network (FCFPN). Specifically, a modal complementation strategy was introduced to bridge the discrepancies between cross-modal features and facilitate their alignment. Additionally, a cross-modal feature projection mechanism was employed to embed modality-correlated features into the shared feature space, thereby mitigating feature loss caused by modality differences. Furthermore, multi-channel and multi-level features were extracted from the shared space to enhance the overall feature representation. Extensive experimental results demonstrated that the proposed FCFPN model effectively mitigated the modal discrepancy, achieving 84.7% Rank-1 accuracy and 86.9% mAP in the indoor test mode of the SYSU-MM01 dataset, and 93.0% Rank-1 accuracy and 87.3% mAP in the VIS-to-IR test mode of the RegDB dataset, thereby outperforming several state-of-the-art methods.