Face Recognition Model

Face recognition models aim to automatically identify individuals from their facial images, with applications ranging from security systems to personalized services. Current research heavily focuses on mitigating biases (especially racial and age biases) through techniques like data augmentation with synthetic data generated by diffusion models and knowledge distillation, as well as improving robustness to real-world conditions such as occlusions, poor image quality, and the presence of face masks. These advancements are crucial for ensuring fairness, accuracy, and trustworthiness in face recognition systems, impacting both the scientific understanding of these models and their ethical deployment in various applications.