Driver Drowsiness Detection System Using Machine Learning Technique

Drowsiness and fatigue are significant contributors to road accidents. We can prevent them by ensuring adequate sleep before driving, consuming caffeine, or taking rest breaks when drowsiness symptoms appear Current methods for detecting drowsiness, such as EEG and ECG, are accurate but require contact measurement and have limitations for real-time monitoring while driving. Proposes using eye closing rate and yawning as indicators for detecting drowsiness in drivers, as a non-invasive and comfortable alternative the goal of this paper is to create a non-invasive system that can detect fatigue in humans and provide timely warnings. Long distance drivers who tend to not take breaks in between are always at a high risk of drowsiness. The primary behavioral indicators used in the suggested technique are the driver’s yawning and eye blinking. The purpose of this Problem is to alert the driver by detecting yawning via closed eyes or an opened mouth.