Real-Time Drowsiness Detection Using Eye Aspect Ratio and Facial Landmark Detection

TL;DR

This paper introduces a real-time drowsiness detection system that uses facial landmarks and eye aspect ratio to accurately identify drowsiness, with potential applications in transportation and workplace safety.

Contribution

The study presents a novel real-time drowsiness detection method combining facial landmark detection and EAR, demonstrating high accuracy with low computational requirements.

Findings

High detection accuracy in real-time scenarios

Low computational load suitable for embedded systems

Effective identification of eye closure indicating drowsiness

Abstract

Drowsiness detection is essential for improving safety in areas such as transportation and workplace health. This study presents a real-time system designed to detect drowsiness using the Eye Aspect Ratio (EAR) and facial landmark detection techniques. The system leverages Dlibs pre-trained shape predictor model to accurately detect and monitor 68 facial landmarks, which are used to compute the EAR. By establishing a threshold for the EAR, the system identifies when eyes are closed, indicating potential drowsiness. The process involves capturing a live video stream, detecting faces in each frame, extracting eye landmarks, and calculating the EAR to assess alertness. Our experiments show that the system reliably detects drowsiness with high accuracy while maintaining low computational demands. This study offers a strong solution for real-time drowsiness detection, with promising applications in driver monitoring and workplace safety. Future research will investigate incorporating additional physiological and contextual data to further enhance detection accuracy and reliability.