Citation

Chai, Chin Heng and Abdul Razak, Siti Fatimah and Yogarayan, Sumendra and Shanmugam, Ramesh (2025) Drunk Driver Detection Using Thermal Facial Images. Information, 16 (5). p. 413. ISSN 2078-2489

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Abstract

This study aims to investigate and propose a machine learning approach that can accurately detect alcohol consumption by analyzing the thermal patterns of facial features. Thermal images from the Tufts Face Database and self-collected images were utilized to train the models in identifying temperature variations in specific facial regions. Convolutional Neural Networks (CNNs) and YOLO (You Only Look Once) algorithms were employed to extract facial features, while classifiers such as Support Vector Machines (SVMs), Multi-Layer Perceptron (MLP), and K-Nearest Neighbors (KNN), as well as Random Forest and linear regression, classify individuals as sober or intoxicated based on their thermal images. The models’ effectiveness in analyzing thermal images to determine alcohol intoxication is expected to provide a foundation for the development of a realistic drunk driver detection system based on thermal images. In this study, MLP obtained 90% accuracy and outperformed the other models in classifying the thermal images, either as sober or showing signs of alcohol consumption. The trained models may be embedded in advanced drunk detection systems as part of an in-vehicle safety application.

Item Type:	Article
Uncontrolled Keywords:	drunk detection; thermal analysis; facial feature analysis
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	30 Jun 2025 02:09
Last Modified:	30 Jun 2025 02:09
URII:	http://shdl.mmu.edu.my/id/eprint/14140

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