Abstract
Cloth-Changing Person Re-identification (CC-ReID) aims to solve the challenge of identifying individuals across different temporal-spatial scenarios, viewpoints, and clothing variations. This field is gaining increasing attention in big data research and public security domains. Existing ReID research primarily relies on face recognition, gait semantic recognition, and clothing-irrelevant feature identification, which perform relatively well in scenarios with high-quality clothing change videos and images. However, these approaches depend on either single features or simple combinations of multiple features, making further performance improvements difficult. Additionally, limitations such as missing facial information, challenges in gait extraction, and inconsistent camera parameters restrict the broader application of CC-ReID. To address the above limitations, we innovatively propose a Tri-Stream Dynamic Weight Network (TSDW) that requires only images. This dynamic weighting network consists of three parallel feature streams: facial features, head-limb features, and global features. Each stream specializes in extracting its designated features, after which a gating network dynamically fuses confidence levels. The three parallel feature streams enhance recognition performance and reduce the impact of any single feature failure, thereby improving model robustness. Extensive experiments on benchmark datasets (e.g., PRCC, Celeb-reID, VC-Clothes) demonstrate that our method significantly outperforms existing state-of-the-art approaches.
Abstract (translated)
服装变化人员重识别（CC-ReID）旨在解决跨不同时空场景、视角和着装变化时个体身份的识别挑战。这一领域在大数据研究和公共安全领域正日益受到关注。现有的ReID研究主要依赖于面部识别、步态语义识别以及与衣物无关特征的识别，这些方法在高质量的服装变化视频和图像场景中表现相对较好。然而，这些方法要么仅依靠单一特征，要么简单地组合多个特征，这使得进一步提升性能变得困难。此外，缺乏面部信息、步态提取挑战以及摄像机参数不一致等问题也限制了CC-ReID更广泛的应用。 为了解决上述限制，我们创新性地提出了一种三流动态权重网络（TSDW），该方法仅需要图像作为输入。这一动态加权网络由三个并行的特征流组成：面部特征、头部与肢体特征以及全局特征。每个特征流专门负责提取其指定的信息，在此之后通过一个门控网络动态融合置信度级别。这三个平行特征流增强了识别性能，并减少了任何单一特征失效对整体性能的影响，从而提高了模型的鲁棒性。 在基准数据集（如PRCC、Celeb-reID、VC-Clothes）上的广泛实验表明，我们提出的方法显著优于现有最先进的方法。
URL
https://arxiv.org/abs/2503.00477
