Facial Landmark-Based System for Early Detection of Driver Fatigue and Safety Alerts

Abstract: Drowsy driving causes road accidents with severe consequences, and drowsiness causes sensory and cognitive impairments. Current systems, however, often base detection on vehicle data and ignore the direct behavior of the driver. Through this project, a non-invasive camera system is introduced that detects driver drowsiness using computer vision to monitor facial features, especially eye and mouth closure. The system detects the driver's faces, extracts facial landmarks, computes the Eye Aspect Ratio (EAR), and computes the Mouth Opening Ratio (MOR) using static and dynamic thresholding techniques. Abnormal EAR or MOR or a hand gesture such as head nodding or mouth covering will immediately trigger an alert for warning the driver. The system thus provides an inexpensive and easy-to-implement solution for increased road safety.