Driver Drowsiness Detection Using MultiChannel Second Order Blind Identifications

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.