Breaking Tradition With Perception: Debiasing Strategies in Cloth-Changing Person Re-Identification

Person ReID aims to match images of individuals captured from different camera views for identity retrieval. Traditional ReID methods primarily rely on clothing features, assuming that individuals do not change clothes in a short time frame. This assumption significantly reduces recognition accuracy when clothing changes, particularly in long-term ReID tasks cloth-changing person re-identification (CC-ReID). Thus, achieving effective re-identification in clothing-change scenarios has become a critical challenge. This paper proposes an automatic perception model (APM) to address the break posed by clothing changes. The model uses a dual-branch with a dynamic perception learning (DPL) strategy and a perception branch, minimizing the bias introduced by clothing on identity recognition while preserving semantic features. The DPL strategy dynamically adjusts training weights to enhance the model's ability to learn from varying sample difficulties and feature distributions. The perception branch captures deeper feature relationships, alleviating the impact of clothing bias and improving the model's ability to distinguish intra-class transformations. Validated on Celeb-Reid and Celeb-Reid-light datasets, APM achieves a mean average precision (mAP) of 22.6% and 25.9%, with Rank-1 accuracy of 77.3% and 79.5%, respectively. It also excels in short-term ReID, achieving 90% mAP and 96.3% Rank-1 accuracy on Markt1501, demonstrating robustness across scenarios.