Disentangling Identity Features from Interference Factors for Cloth-Changing Person Re-identification

Cloth-Changing Person Re-Identification (CC-ReID) aims to accurately identify a target person in the more realistic surveillance scenario where clothes of the pedestrian may change drastically, which is critical in public security systems for tracking down disguised criminal suspects. Existing methods mainly transform the CC-ReID problem into cross-modality feature alignment from the data-driven perspective, without modelling the interference factors such as clothes and camera view changes meticulously. This may lead to over-consideration or under-consideration of the influence of these factors on the extraction of robust and discriminative identity features. This paper proposes a novel algorithm for thoroughly disentangling identity features from interference factors brought by clothes and camera view changes while ensuring the robustness and discriminability. It adopts a dual-stream identity feature learning framework consisting of a raw image stream and a cloth-erasing stream, to explore discriminative and cloth-irrelevant identity feature representations. Specifically, an adaptive cloth-irrelevant contrastive objective is introduced to contrast features extracted by the two streams, aiming to suppress the fluctuation caused by clothes textures in the identity feature space. Moreover, we innovatively mitigate the influence of the interference factors through a generative adversarial interference factor decoupling network. This network is targeted at capturing identity-related information residing in the interference factors and disentangling the identity features from such information. Extensive experimental results demonstrate the effectiveness of the proposed method, achieving superior performances to state-of-the-art methods.