Micro-Expressions for Safer Roads: Deep Learning-Based Drowsiness Detection in Truck Drivers

Abstract: Drowsy driving is a major cause of traffic accidents, especially among truck drivers who work long hours with limited sleep. Although contemporary driver monitoring systems have employed deep learning and computer vision, many depend on obvious signs like eye closure, yawning, or head nodding, which emerge only after considerable fatigue has developed. This research suggests a novel system that utilizes micro-expressions—slight, unintentional facial movements—as initial signs of drowsiness. Micro-expressions are more reliably identified than explicit signals, allowing for earlier intervention to avert accidents. We developed a deep learning model using a specialized dataset aimed at capturing micro-expressions and enhanced it through pre-processing and augmentation methods to boost generalization. The system underwent testing with truck drivers, who experience increased fatigue risks stemming from extended hours, repetitive routes, and substantial vehicle loads. Our findings show more than 95% accuracy, exceeding current methods and validating the system's ability to enhance road safety by detecting fatigue early.

Drowsy driving, Truck drivers, Micro-expressions, Deep learning, Driver monitoring systems, Facial analysis, Early drowsiness detection, Road safety, Computer vision, Real-time alert system

1. Abtahi, S., Hariri, B., & Shirmohammadi, S. (2011, May). Driver drowsiness monitoring based on yawning detection. In 2011 IEEE international instrumentation and measurement technology conference (pp. 1-4). [Google Scholar] [Crossref]

2. Bergasa, L. M., Nuevo, J., Sotelo, M. A., Barea, R., & Lopez, M. E. (2006). Real-time system for monitoring driver vigilance. IEEE Transactions on intelligent transportation systems, 7(1), 63-77. [Google Scholar] [Crossref]

3. Dong, Y., Hu, Z., Uchimura, K., & Murayama, N. (2010). Driver inattention monitoring system for intelligent vehicles: A review. IEEE transactions on intelligent transportation systems, 12(2), 596-614. [Google Scholar] [Crossref]

4. Mandal, B., Li, L., Wang, G. S., & Lin, J. (2016). Towards detection of bus driver fatigue based on robust visual analysis of eye state. IEEE Transactions on Intelligent Transportation Systems, 18(3), 545-557. [Google Scholar] [Crossref]

5. Picot, A., Charbonnier, S., & Caplier, A. (2011). On-line detection of drowsiness using brain and visual information. IEEE Transactions on systems, man, and cybernetics-part A: systems and humans, 42(3), 764-775. [Google Scholar] [Crossref]

6. Sahayadhas, A., Sundaraj, K., & Murugappan, M. (2012). Detecting driver drowsiness based on sensors: a review. Sensors, 12(12), 16937-16953. [Google Scholar] [Crossref]

7. Vicente, J., Laguna, P., Bartra, A., & Bailón, R. (2016). Drowsiness detection using heart rate variability. Medical & biological engineering & computing, 54(6), 927-937. [Google Scholar] [Crossref]

8. Deng, W., & Wu, R. (2019). Real-time driver-drowsiness detection system using facial features. Ieee Access, 7, 118727-118738. [Google Scholar] [Crossref]

9. Wali, M. K., Murugan, S., & George, S. T. (2018). Micro-expression recognition for driver drowsiness detection. International Journal of Pure and Applied Mathematics, 118(20), 4811–4824. [Google Scholar] [Crossref]

10. Du, Y., Ma, P., Su, X., & Zhang, Y. (2008, December). Driver fatigue detection based on eye state analysis. In 11th Joint International Conference on Information Sciences (pp. 132-137). Atlantis Press. [Google Scholar] [Crossref]

11. Abidin, A. Z., Nugroho, H. A., & Heryanto, A. I. (2019). Driver fatigue detection system using facial micro-expression and eye blink. Journal of Physics: Conference Series, 1201(1), 012044. [Google Scholar] [Crossref]

12. Lal, S. K., & Craig, A. (2001). A critical review of the psychophysiology of driver fatigue. Biological psychology, 55(3), 173-194. [Google Scholar] [Crossref]

13. Lee, B. G., & Chung, W. Y. (2012). Driver alertness monitoring using fusion of facial features and bio-signals. IEEE Sensors journal, 12(7), 2416-2422. [Google Scholar] [Crossref]

14. Zhao, L., Wang, Z., Wang, X., & Liu, Q. (2018). Driver drowsiness detection using facial dynamic fusion information and a DBN. IET Intelligent Transport Systems, 12(2), 127-133. [Google Scholar] [Crossref]

15. Malla, R. K., & Davidson, T. (2019). Drowsy and distracted driving detection system. Sensors, 19(23), 5301. [Google Scholar] [Crossref]

16. Marín-Morales, J., Higuera-Trujillo, J. L., Greco, A., Guixeres, J., Llinares, C., Scilingo, E. P., ... & Valenza, G. (2018). Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Scientific reports, 8(1), 13657. [Google Scholar] [Crossref]

17. Schleicher, R., Galley, N., Briest, S., & Galley, L. (2008). Blinks and saccades as indicators of fatigue in sleepiness warnings: looking tired?. Ergonomics, 51(7), 982-1010. [Google Scholar] [Crossref]

18. Savaş, B. K., & Becerikli, Y. (2018, October). Real time driver fatigue detection based on SVM algorithm. In 2018 6th International Conference on Control Engineering & Information Technology (CEIT) (pp. 1-4). IEEE. [Google Scholar] [Crossref]

19. Al-Mannai, K., Al-Thelaya, K., Schneider, J., & Bakiras, S. (2024). Multimodal Face Data Sets—A Survey of Technologies, Applications, and Contents. IEEE Access, 12, 183616-183657. [Google Scholar] [Crossref]

20. Malik, P., Singh, J., Ali, F., Sehra, S. S., & Kwak, D. (2025). Action unit based micro-expression recognition framework for driver emotional state detection. Scientific Reports, 15(1), 27824. [Google Scholar] [Crossref]

21. Zhang, J. (2023, August). Human-computer Interaction for Driver Fatigue Detection through Micro-expressions. In 2023 IEEE International Conference on Sensors, Electronics and Computer Engineering (ICSECE) (pp. 208-212). IEEE. [Google Scholar] [Crossref]

22. Harris, L. (2025). AI-Enabled Recognition of Odors and Images for Assessing Driver Fatigue and Cabin Environment. [Google Scholar] [Crossref]

23. Samy, S., Gedam, V. V., Karthick, S., Sudarsan, J. S., Anupama, C. G., Ghosal, S., & Samuel, A. A. (2025). The drowsy driver detection for accident mitigation using facial recognition system. Life Cycle Reliability and Safety Engineering, 1-13. [Google Scholar] [Crossref]

• Advanced Techniques for Fake News Detection on Twitter Using NLP and AI: A Comprehensive Review
• Review of Self Compacting Geopolymer Concrete Using Slag Sand as Fine Aggregate
• Performance of Local Construction Contractors – Case Study of Registered Contractors in Monrovia, Liberia
• Cross-Cultural Perspectives on Innovation Management in Multinational Organizations
• Modeling of Reaction Between Dissolved Oxygen (DO) And Biological Oxygen Demand (BOD) in Degradation River