Deep Learning Driven Driver Fatigue Detection Using CNN and Temporal Eye Dynamics

Author(s)	Ms. Nagashree K S
Country	India
Abstract	Driver fatigue is one of the leading causes of road accidents, posing a serious threat to transportation safety worldwide. To address this issue, intelligent and automated monitoring systems are essential for detecting early signs of drowsiness. Recent advances in deep learning and computer vision enable real-time analysis of a driver’s facial behavior using ordinary cameras. By leveraging techniques such as eye and mouth movement analysis along with distraction detection, fatigue can be identified promptly, allowing timely alerts and interventions to reduce accident risks and improve overall road safety.
Keywords	Driver Fatigue Detection, Deep Learning, CNN, Computer Vision, Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), Facial Landmark Detection, YOLO, Driver Distraction Detection, Real-Time Monitoring, Intelligent Transportation Systems
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-15
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.65044

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