MSP-ReID: Hairstyle-Robust Cloth-Changing Person Re-Identification

TL;DR

This paper introduces MSP, a framework for cloth-changing person re-identification that enhances robustness by reducing hairstyle bias and preserving structural cues through augmentation, erasing, and parsing attention.

Contribution

MSP is the first comprehensive approach combining hairstyle augmentation, clothing region erasing, and parsing-guided attention to improve cloth-changing person re-identification.

Findings

Achieves state-of-the-art results on CC-ReID benchmarks.

Effectively reduces hairstyle dependence in re-identification.

Maintains structural integrity while suppressing clothing texture bias.

Abstract

Cloth-Changing Person Re-Identification (CC-ReID) aims to match the same individual across cameras under varying clothing conditions. Existing approaches often remove apparel and focus on the head region to reduce clothing bias. However, treating the head holistically without distinguishing between face and hair leads to over-reliance on volatile hairstyle cues, causing performance degradation under hairstyle changes. To address this issue, we propose the Mitigating Hairstyle Distraction and Structural Preservation (MSP) framework. Specifically, MSP introduces Hairstyle-Oriented Augmentation (HSOA), which generates intra-identity hairstyle diversity to reduce hairstyle dependence and enhance attention to stable facial and body cues. To prevent the loss of structural information, we design Cloth-Preserved Random Erasing (CPRE), which performs ratio-controlled erasing within clothing regions to suppress texture bias while retaining body shape and context. Furthermore, we employ Region-based Parsing Attention (RPA) to incorporate parsing-guided priors that highlight face and limb regions while suppressing hair features. Extensive experiments on multiple CC-ReID benchmarks demonstrate that MSP achieves state-of-the-art performance, providing a robust and practical solution for long-term person re-identification.