Driver drowsiness detection is crucial to preventing road accidents caused by fatigue. This paper proposes a non-intrusive, real-time system based on facial landmark detection and the Eye Aspect Ratio (EAR) using a standard webcam. The system continuously monitors eye activity and triggers an alert when signs of drowsiness are detected, measured by sustained low EAR values. The approach integrates a pre-trained face detector, facial landmark predictor, and an EAR-based thresholding mechanism to determine eye closure. High accuracy is demonstrated by the experimental results in detecting drowsiness, making the system suitable for embedded or mobile deployment in automotive applications.
Fathima et al. (Thu,) studied this question.