Driver Fatigue Detection Based on Facial Recognition

DOI

10.2991/978-94-6463-821-9_49How to use a DOI?

Keywords

Recognition; Fatigue Detection; Deep Learning; Driving Safety

Abstract

Fatigue driving poses a significant threat to road safety, and monitoring driver alertness is essential for preventing accidents. This paper explores fatigue driving monitoring technology using facial recognition, which relies on non-contact visual analysis to detect fatigue based on key facial features such as eyelid closure, mouth movements, and head posture. This approach outperforms traditional methods such as physiological signal monitoring and vehicle behavior analysis, offering advantages in non-invasiveness, real-time detection, and improved accuracy in fatigue detection. Unlike physiological signal-based systems, which require invasive sensors, and vehicle behavior analysis that often suffers from latency, facial recognition technology can deliver timely alerts with high precision. Although significant progress has been made, challenges such as environmental interference and individual adaptability persist. Future advancements should focus on lightweight algorithms, the integration of multi-sensor data, and enhancing the generalization capabilities of deep learning models to ensure broader application in active safety systems. This paper provides insights into the current state of fatigue detection and outlines the key challenges and future directions for the technology’s development.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiaming Zhang
PY  - 2025
DA  - 2025/08/31
TI  - Driver Fatigue Detection Based on Facial Recognition
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 480
EP  - 489
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_49
DO  - 10.2991/978-94-6463-821-9_49
ID  - Zhang2025
ER  -