Visible-Infrared Person Re-Identification via Modality Disentanglement and Local Discriminative Enhancement

Abstract

Visible-Infrared Person Re-Identification (VI-ReID) is essential for all-weather surveillance in smart city applications. Existing methods still face two critical bottlenecks. First, significant modal discrepancies between visible and infrared images lead to serious distribution shifts, causing identity-irrelevant modality information to interfere with identity semantics. Second, decoupled shared features often lack focus on discriminative local regions, which limits the model's ability to capture fine-grained details. To address these issues, we propose a novel framework termed Modality Disentanglement and Local Discriminative Enhancement (MDLDE). We first introduce a disentanglement method based on Mutual Information Minimization to minimize statistical dependence between modality-shared and modality-specific features from a probability distribution perspective. Subsequently, a Local Discriminative Attention Module is designed to adaptively focus on highly informative body parts such as head-shoulder ratio and torso patterns. By reinforcing these localized cues, the model achieves robust fine-grained representations against complex backgrounds. Extensive experiments on the SYSU-MM01 datasets demonstrate the superiority of our method. On the SYSU-MM01 All-Search mode, we achieve 73.9% Rank-1 and 69.7% mAP, outperforming current state-of-the-art approaches.