Enhancing driver safety : Real-time eye detection for drowsiness prevention driver assistance systems †

Drowsiness has become a significant contributing factor to traffic accidents in modern times, posing a major concern to society. Driver fatigue or sleepiness leads to decreased reaction time, diminished attention, and compromised decision-making abilities, thereby affecting the overall driving experience. This paper addresses this issue by proposing a drowsiness detection system based on image processing, utilizing a cascade of classifiers built on Haar-like features. The system effectively detects the eyes, allowing for determination of eye closure or opening, which serves as an indicator of driver drowsiness.