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Applied Sciences
Volume 15
Issue 3
10.3390/app15031102
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Article

AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis

by
Tahesin Samira Delwar
1,†,
Mangal Singh
2,†,
Sayak Mukhopadhyay
2,
Akshay Kumar
2,
Deepak Parashar
3,
Yangwon Lee
4,
Md Habibur Rahman
5,6,
Mohammad Abrar Shakil Sejan
7,* and
Jee Youl Ryu
1,*
1
Department of Information and Communications Engineering, Pukyong National University, Busan 48513, Republic of Korea
2
Department of Electronics and Telecommunication Engineering, Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune 412115, India
3
Department of Computer Science & Engineering, GSFC University, Vadodara 391650, India
4
Department of Spatial Engineering, Pukyong National University, Busan 48513, Republic of Korea
5
Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
6
Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea
7
Department of Electrical Engineering, Sejong University, Seoul 05006, Republic of Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2025, 15(3), 1102; https://doi.org/10.3390/app15031102
Submission received: 11 December 2024 / Revised: 15 January 2025 / Accepted: 20 January 2025 / Published: 22 January 2025
(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)
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Abstract

The significant number of road traffic accidents caused by fatigued drivers presents substantial risks to the public’s overall safety. In recent years, there has been a notable convergence of intelligent cameras and artificial intelligence (AI), leading to significant advancements in identifying driver drowsiness. Advances in computer vision technology allow for the identification of driver drowsiness by monitoring facial expressions such as yawning, eye movements, and head movements. These physical indications, together with assessments of the driver’s physiological condition and behavior, aid in assessing fatigue and lowering the likelihood of drowsy driving-related incidents. This study presents an extensive variety of meticulously designed algorithms that were thoroughly analyzed to assess their effectiveness in detecting drowsiness. At the core of this attempt lay the essential concept of feature extraction, an efficient technique for isolating facial and ocular regions from a particular set of input images. Following this, various deep learning models, such as a traditional CNN, VGG16, and MobileNet, facilitated detecting drowsiness. Among these approaches, the MobileNet model was a valuable choice for drowsiness detection in drivers due to its real-time processing capability and suitability for deployment in resource-constrained environments, with the highest achieved accuracy of 92.75%.
Keywords: classification; computer vision; deep learning; drowsiness detection; MobileNet; traditional CNN; VGG16 classification; computer vision; deep learning; drowsiness detection; MobileNet; traditional CNN; VGG16

Share and Cite

MDPI and ACS Style

Delwar, T.S.; Singh, M.; Mukhopadhyay, S.; Kumar, A.; Parashar, D.; Lee, Y.; Rahman, M.H.; Sejan, M.A.S.; Ryu, J.Y. AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis. Appl. Sci. 2025, 15, 1102. https://doi.org/10.3390/app15031102

AMA Style

Delwar TS, Singh M, Mukhopadhyay S, Kumar A, Parashar D, Lee Y, Rahman MH, Sejan MAS, Ryu JY. AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis. Applied Sciences. 2025; 15(3):1102. https://doi.org/10.3390/app15031102

Chicago/Turabian Style

Delwar, Tahesin Samira, Mangal Singh, Sayak Mukhopadhyay, Akshay Kumar, Deepak Parashar, Yangwon Lee, Md Habibur Rahman, Mohammad Abrar Shakil Sejan, and Jee Youl Ryu. 2025. "AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis" Applied Sciences 15, no. 3: 1102. https://doi.org/10.3390/app15031102

APA Style

Delwar, T. S., Singh, M., Mukhopadhyay, S., Kumar, A., Parashar, D., Lee, Y., Rahman, M. H., Sejan, M. A. S., & Ryu, J. Y. (2025). AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis. Applied Sciences, 15(3), 1102. https://doi.org/10.3390/app15031102

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