AI RESEARCH
Improving Driver Drowsiness Detection via Personalized EAR/MAR Thresholds and CNN-Based Classification
arXiv CS.CV
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ArXi:2604.22479v1 Announce Type: new Driver drowsiness is a major cause of traffic accidents worldwide, posing a serious threat to public safety. Vision-based driver monitoring systems often rely on fixed Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) thresholds; however, such fixed values frequently fail to generalize across individuals due to variations in facial structure, illumination, and driving conditions.