Drunk Driver Detection Using Thermal Facial Images
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 temperatu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/16/5/413 |
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| author | Chin-Heng Chai Siti Fatimah Abdul Razak Sumendra Yogarayan Ramesh Shanmugam |
| author_facet | Chin-Heng Chai Siti Fatimah Abdul Razak Sumendra Yogarayan Ramesh Shanmugam |
| author_sort | Chin-Heng Chai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1e85f923bde44fb383a59ea1b7d33996 |
| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-1e85f923bde44fb383a59ea1b7d339962025-08-20T01:56:28ZengMDPI AGInformation2078-24892025-05-0116541310.3390/info16050413Drunk Driver Detection Using Thermal Facial ImagesChin-Heng Chai0Siti Fatimah Abdul Razak1Sumendra Yogarayan2Ramesh Shanmugam3Faculty of Information Science and Technology, Multimedia University, Melaka 75450, MalaysiaFaculty of Information Science and Technology, Multimedia University, Melaka 75450, MalaysiaFaculty of Information Science and Technology, Multimedia University, Melaka 75450, MalaysiaDepartment of Mechatronics Engineering, Rajalakshmi Engineering College, Thandalam 602105, IndiaThis 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.https://www.mdpi.com/2078-2489/16/5/413drunk detectionthermal analysisfacial feature analysis |
| spellingShingle | Chin-Heng Chai Siti Fatimah Abdul Razak Sumendra Yogarayan Ramesh Shanmugam Drunk Driver Detection Using Thermal Facial Images Information drunk detection thermal analysis facial feature analysis |
| title | Drunk Driver Detection Using Thermal Facial Images |
| title_full | Drunk Driver Detection Using Thermal Facial Images |
| title_fullStr | Drunk Driver Detection Using Thermal Facial Images |
| title_full_unstemmed | Drunk Driver Detection Using Thermal Facial Images |
| title_short | Drunk Driver Detection Using Thermal Facial Images |
| title_sort | drunk driver detection using thermal facial images |
| topic | drunk detection thermal analysis facial feature analysis |
| url | https://www.mdpi.com/2078-2489/16/5/413 |
| work_keys_str_mv | AT chinhengchai drunkdriverdetectionusingthermalfacialimages AT sitifatimahabdulrazak drunkdriverdetectionusingthermalfacialimages AT sumendrayogarayan drunkdriverdetectionusingthermalfacialimages AT rameshshanmugam drunkdriverdetectionusingthermalfacialimages |