Labeled Face

Labeled face research focuses on developing and improving automated face recognition systems, addressing challenges like bias in existing datasets, the vulnerability of these systems to manipulation (e.g., morphing attacks), and the need for accurate recognition across diverse demographics and ages, including children. Current research emphasizes creating more representative datasets, exploring novel loss functions and model architectures (like contrastive learning) for improved accuracy and robustness, and developing methods for detecting manipulated images. This work is crucial for enhancing the reliability and fairness of facial recognition technologies, with implications for security, law enforcement, and various other applications.