Large Scale Face Recognition

Large-scale face recognition aims to develop accurate and efficient systems for identifying individuals from vast datasets of facial images. Current research focuses on improving training efficiency, often using single-GPU solutions and novel learning rate schedulers, while simultaneously addressing ethical concerns by exploring synthetic data generation and knowledge distillation techniques to reduce reliance on real-world datasets. These advancements are crucial for improving the accuracy and scalability of face recognition systems, impacting applications ranging from security and law enforcement to personalized services and biometric authentication. Furthermore, research is actively addressing challenges like backward compatibility in model updates and handling class imbalance in training data.