Full Face

Full-face analysis in computer vision focuses on developing robust and accurate systems for processing and interpreting images of entire faces, addressing challenges like bias mitigation, privacy preservation, and handling variations in lighting, occlusion, and pose. Current research emphasizes improving model robustness through techniques like data augmentation (including synthetic data generation), fairness-enhancing metric learning, and knowledge distillation methods, often employing convolutional neural networks and transformer-based architectures. These advancements are crucial for enhancing the reliability and ethical implications of applications ranging from facial recognition and gaze estimation to diagnosing skin conditions and understanding human behavior.