Abstract: This project presents a comprehensive, real-time driver drowsiness detection and alert system using facial landmark analysis, remote photoplethysmography (rPPG), and machine learning. The system captures live video through a webcam and extracts key features such as Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), head pose angles, and heart rate using non-contact methods. A deep learning model processes these indicators to accurately classify driver alertness. Upon detecting drowsiness, the system immediately triggers audio-visual alerts to regain driver attention. Designed for non-intrusive monitoring and ease of deployment, this tool aims to enhance road safety and reduce fatigue-related accidents. The system also includes a GUI for usability and can be extended with additional safety interventions.

Index Terms—Driver Drowsiness Detection, Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), Head Pose Estimation, Heart Rate Monitoring, Remote Photoplethysmography (rPPG), Deep Learning, Support Vector Machine (SVM), Real-Time Alert System, Facial Landmark Detection, Driver Safety.

Keywords: Detecting Driver Drowsiness

| DOI: 10.17148/IARJSET.2025.125329