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Authors: Amina Turki 1 ; Sirine Ammar 2 ; Mohamed Karray 3 and Mohamed Ksantini 1

Affiliations: 1 Control & Energies Management Laboratory (CEM-Lab), National Engineering School of Sfax, University of Sfax, Tunisia ; 2 National School of Electronics and Telecommunications of Sfax, University of Sfax, Tunisia ; 3 ESME Research Lab, Special School of Mechanics and Electricity (ESME), Ivry Sur Seine, France

Keyword(s): Driver Drowsiness Detection (DDD) System, Deep Neural Networks (DNNs), the Chebyshev Distance.

Abstract: Driver drowsiness is a leading cause of road accidents, resulting in severe physical injuries, fatalities, and substantial economic losses. To address this issue, a sophisticated Driver Drowsiness Detection (DDD) system is needed to alert the driver in case of abnormal behaviour and prevent potential catastrophes. The proposed DDD system calculates the Eyes Closure Ratio (ECR) and Mouth Opening Ratio (MOR) using the Chebyshev distance, instead of the classical Euclidean distance, to model the driver’s behaviour and to detect drowsiness states. This system uses simple camera and deep transfer learning techniques to detect the driver’s drowsiness state and then alert the driver in real time situations. The system achieves 96% for the VGG19 model, and 98% for the ResNet50 model, with a precision rate of 98% in assessing the driver’s dynamics.

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Paper citation in several formats:
Turki, A.; Ammar, S.; Karray, M. and Ksantini, M. (2024). Facial Expression-Based Drowsiness Detection System for Driver Safety Using Deep Learning Techniques. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 726-733. DOI: 10.5220/0012386000003636

@conference{icaart24,
author={Amina Turki. and Sirine Ammar. and Mohamed Karray. and Mohamed Ksantini.},
title={Facial Expression-Based Drowsiness Detection System for Driver Safety Using Deep Learning Techniques},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={726-733},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012386000003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Facial Expression-Based Drowsiness Detection System for Driver Safety Using Deep Learning Techniques
SN - 978-989-758-680-4
IS - 2184-433X
AU - Turki, A.
AU - Ammar, S.
AU - Karray, M.
AU - Ksantini, M.
PY - 2024
SP - 726
EP - 733
DO - 10.5220/0012386000003636
PB - SciTePress

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