An Adaptive AutoLearning System for Camera-Based Facial Recognition

In the rapidly evolving field of video surveillance and security, there is an urgent need for facial recognition systems that can autonomously adapt and improve their accuracy in real-time, particularly in dynamic environments where new faces frequently appear, and lighting conditions constantly change. This paper addresses the critical challenge of developing self-learning camera systems capable of continuously enhancing facial recognition performance without manual intervention, a requirement that has become increasingly vital for maintaining effective security measures in various sectors including public safety, retail, and smart cities. To tackle this pressing issue, we propose a novel autolearning method that combines RetinaFace for face detection and ArcFace for face recognition, incorporating a self-updating mechanism. Our approach consists of three key steps: (1) automatic collection of high-confidence facial images (>90% accuracy) during recognition tasks, (2) periodic model retraining using the collected data, and (3) iterative model updating to enhance overall accuracy.