Face Clustering

Face clustering aims to automatically group images of the same individual from large, unlabeled datasets, a crucial task for applications like photo organization and face recognition system scaling. Recent research emphasizes developing unsupervised and self-supervised methods, moving away from reliance on complex supervised models and hyperparameter tuning, with a focus on improving graph-based clustering algorithms and refining feature extraction techniques to handle variations in pose, lighting, and resolution. These advancements are improving the accuracy and efficiency of face clustering, enabling better management of massive face image collections and enhancing the performance of related computer vision systems.