Driver Drowsiness Detection using AI

Driver drowsiness is a leading cause of road accidents, often resulting in serious consequences due to delayed human response. This project proposes a real time Driver Drowsiness Detection System that uses facial landmark tracking and behavioural analysis to identify signs of fatigue. The system employs OpenCV and dlib libraries to monitor facial features such as eyes and mouth, calculating metrics like Eye Aspect Ratio (EAR) and blink rate to detect prolonged eye closure, yawning, and head nodding. When drowsiness is detected, the system issues an audio alert and, if necessary, sends an SMS notification to a predefined emergency contact via an integrated API. Designed with affordability and scalability in mind, this solution utilizes standard webcams and open source technologies, making it both practical and accessible. By combining computer vision with real time alert mechanisms, the system enhances road safety, especially for long distance drivers.