Unlabeled Face

Unlabeled face data presents a significant challenge and opportunity in computer vision, driving research into efficient and effective methods for clustering, recognition, and expression analysis without relying on manual labeling. Current research focuses on developing unsupervised and self-supervised learning techniques, employing architectures like capsule networks and graph-based methods, along with innovative strategies such as early stopping, landmark-based feature extraction, and pairwise classification to improve clustering accuracy and representation learning. These advancements are crucial for scaling face recognition systems, enhancing facial expression analysis, and enabling applications in diverse fields like security, human-computer interaction, and emotion AI where labeled data is scarce or expensive to obtain.