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

• Main Authors: Zainah, Md Zain, Mohd Shahril, Roseli, Nurul Athirah, Abdullah
• Format: Article
• Language: English
• Published: Multidisciplinary Digital Publishing Institute (MDPI) 2023
• Subjects: T Technology (General); TA Engineering (General). Civil engineering (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/42810/1/Enhancing%20driver%20safety_Real-time%20eye%20detection%20for%20drowsiness%20prevention%20driver.pdf; http://umpir.ump.edu.my/id/eprint/42810/; https://doi.org/10.3390/engproc2023046039; https://doi.org/10.3390/engproc2023046039

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.