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Driver drowsiness detection using facial thermal imaging in a driving simulator.

Masoumeh Tashakori¹, Ali Nahvi¹, Serajeddin Ebrahimian Hadi Kiashari¹

¹Virtual Reality Laboratory, K.N. Toosi University of Technology, Tehran, Iran.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine

|September 3, 2021

Summary

Driver drowsiness detection is improved by monitoring facial temperature changes. Measuring forehead and cheek temperatures, especially their gradient, offers a robust and sensitive indicator for enhanced road safety.

Keywords:

Drowsiness detection system cheek-forehead skin temperature driving simulator drowsy driving thermal imaging

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Area of Science:

Biomedical Engineering
Computer Vision
Human-Computer Interaction

Background:

Driver drowsiness is a significant cause of fatal road accidents.
Thermal imaging offers a non-invasive and ambient light-robust method for monitoring physiological changes.
Facial temperature patterns can reflect physiological states like drowsiness.

Purpose of the Study:

To investigate the efficacy of thermal imaging in detecting driver drowsiness.
To analyze changes in forehead and cheek temperatures and their gradient in relation to drowsiness levels.
To evaluate machine learning classifiers for automated drowsiness detection using thermal data.

Main Methods:

Utilized a thermal camera to capture facial temperature patterns of 30 subjects in a driving simulator.
Tracked forehead and cheek regions to obtain skin temperatures at varying drowsiness levels.
Employed Support Vector Machine, K-Nearest Neighbor, and regression tree classifiers for drowsiness classification.

Main Results:

Forehead skin temperature decreased by 0.46°C and the cheek-forehead gradient decreased by 0.81°C from wakefulness to extreme drowsiness.
Cheek skin temperature increased by 0.35°C.
The temperature gradient change was approximately 50% more significant than individual temperature changes, leading to 82% detection accuracy.

Conclusions:

Driver drowsiness can be effectively detected by monitoring forehead and cheek temperature signals.
The temperature gradient between the cheek and forehead serves as a more robust and sensitive indicator of drowsiness.
Thermal imaging combined with machine learning presents a promising approach for real-time driver drowsiness detection systems.