Enhancing cloth-changing person re-identification with Silhouette-Keypoint fusion

In recent years, person re-identification (ReID) has experienced significant advancements due to its diverse real-world applications. However, traditional benchmarks often assume consistent attire across captured images, failing to reflect the reality of pedestrians frequently changing their clothing. This discrepancy has led to the emergence of the cloth-changing person re-identification (CC-ReID) problem and the development of relevant benchmarks. CC-ReID poses a substantial challenge, as pedestrians primary visual cues, particularly their clothing, vary between query and gallery images, while non-attire-related features remain relatively weak. To address this gap and advance research in CC-ReID, this paper introduces a novel task termed Silhouette-Keypoint Fusion Re-Identification (SKF-ReID). This represents a dual-stream framework capable of extracting silhouette and keypoint details within the shape stream, subsequently transferring this data to the ReID stream to enrich appearance features with clothing-independent insights. Additionally, we employ the Maximum Mean Discrepancy (MMD) loss to ensure similarity between shape and appearance features, thereby enhancing the accuracy of cloth-changing person re-identification. Our proposed approach undergoes rigorous evaluation across benchmark cloth-changing person re-identification datasets, showcasing cutting-edge performance.