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.
Abstract (translated)
服装变化人员再识别（CC-ReID）旨在跨不同摄像机条件下，在穿着条件不同的情况下匹配同一个人的身份。现有的方法通常会去除衣物，并专注于头部区域以减少着装偏差。然而，这种整体处理头部而不区分脸部和头发的方式导致过分依赖发型线索，从而在发型发生变化时性能下降。 为了解决这个问题，我们提出了减轻发型干扰并保持结构信息的框架（MSP）。具体来说，MSP引入了面向发型的数据增强技术（HSOA），该技术生成同一身份下的发型多样性以减少对发型的依赖，并且突出稳定的面部和身体线索。为了防止丢失结构信息，我们设计了一种保留衣物的随机擦除方法（CPRE），这种方法在服装区域进行比例控制的擦除操作，从而抑制纹理偏差同时保持体型和上下文信息。 此外，我们采用了基于区域解析注意的方法（RPA）来融入解析引导的先验知识，该方法突出面部和四肢区域，并抑制头发特征。在多个CC-ReID基准测试上的广泛实验表明，MSP达到了最先进的性能水平，为长期人员再识别提供了稳健且实用的解决方案。
URL
https://arxiv.org/abs/2603.01640
