Unsupervised Face

Unsupervised face recognition research aims to develop methods for identifying and verifying faces without relying on large, labeled datasets, addressing privacy concerns and data scarcity. Current efforts focus on leveraging techniques like autoencoders, generative adversarial networks (GANs), vision transformers, and capsule networks to learn robust facial representations from unlabeled data, often incorporating self-supervised learning strategies and prototype-based approaches. These advancements are significant because they enable the development of face recognition systems that are more privacy-preserving and adaptable to scenarios with limited annotated data, potentially impacting applications ranging from security to biometric authentication.