Facial Attribute

Facial attribute research focuses on automatically identifying and manipulating various characteristics from facial images, aiming for accurate and unbiased classification and generation. Current research emphasizes mitigating biases in algorithms, particularly concerning demographic attributes, using techniques like fine-grained feature analysis, self-supervised learning, and disentangled generative models (e.g., StyleGAN, diffusion models). This field is crucial for improving fairness in facial recognition systems and enabling applications such as personalized user experiences, medical diagnosis (e.g., detecting neurodevelopmental disorders), and enhanced image editing and generation.