Clothes-Changing Person Re-Identification Using Color and Texture Invariant Representation

This paper tackles the challenge of Clothes-Changing person Re-Identification (CC-ReID) through the dual lenses of model development and dataset creation. Unlike conventional person ReID, which presumes that individuals maintain consistent clothing, CC-ReID acknowledges the frequent changes in attire encountered in real-world scenarios. This task is particularly difficult due to the substantial variations in visual cues such as colors and textures that accompany changes in clothing. We propose a Multi-modal Assisted feature Learning Framework (MAL-F) designed to learn representations that are invariant to color and texture by utilizing RGB, grayscale, and contour images. MAL-F is a versatile framework that can be seamlessly integrated with existing CC-ReID models, significantly enhancing their accuracy. To further reduce the impact of clothing variations, we introduce a novel CC-ReID backbone named ResTNet, which combines ResNet with a Transformer. ResTNet features a Non-Clothing Salient Region (NC-SR) reinforcement transformer module that employs random clothing block drop and spatial activation mapping. These methods direct the model to emphasize key regions unrelated to clothing, enhancing these features with minimal computational overhead. This strategy substantially enhances CC-ReID performance and supports real-time video processing. Furthermore, recognizing the limitations of current small-scale CC-ReID datasets, we have developed two larger datasets: FBCC and Market1501-CC. Extensive experimental results demonstrate that our proposed methodology surpasses existing state-of-the-art methods for CC-ReID in terms of accuracy, robustness, and efficiency.