An Adaptive AutoLearning System for Camera-Based Facial Recognition
Phuong Anh Nguyen, Tung Son Vu, Thuan Minh Bui, Le Anh Ngoc · Advances in computational intelligence and robotics book series · 2025
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.