DRIVER DROWSINESS DETECTION SYSTEM WITH ALERT USING COMPUTER VISION

Abstract

Drowsy driving is an increasing cause of road accidents, with driver fatigue a major contributing factor. This research aims to identify driver drowsiness in real driving conditions to help prevent such accidents. Unlike previous detection methods, this work provides an automated interface that uses webcam images to monitor alertness. By analyzing live video for facial and eye movement features using DLib and EAR, the system infers driver drowsiness. An escalating alarm notifies the driver, and, if unresponsive, the system sends notifications to designated family members. This solution provides real-time drowsiness detection and proactive safety alerts. Keywords: Eye extraction, DLib, Facial Landmarks, Drowsiness, Machine Learning, EAR, Python, Face Detection.