Design of Driver Fatigue Detection Early Warning System Based on Facial Multi-feature Fusion

In this paper, a driver fatigue warning system based on facial multi-feature fusion is designed to monitor real-time fatigue by fusing features such as eye closure, mouth opening and head posture, combined with deep learning algorithms. The system uses a high-definition camera to capture images, and locates and recognizes key facial features by convolutional neural network (CNN) to calculate fatigue indicators such as PERCLOS and MAR. When the fatigue signal reaches a preset threshold, the system triggers an alert to remind the driver. Experimental results show that the system excels in fatigue detection accuracy and realtime performance, and can effectively improve driving safety.