Multi‐Fatigue Feature Selection and Fuzzy Logic‐Based Intelligent Driver Drowsiness Detection - Arava, Mohan; Sundaram, Divya Meena (IET image processing, 2025)
Driver drowsiness poses a critical threat, frequently resulting in highly perilous traffic accidents. The drowsiness detection is complicated by various challenges such as lighting conditions, occluded facial features, eyeglasses, and false alarms, making the accuracy, robustness across environments, and computational efficiency a major challenge. This study proposes a non‐intrusive driver drowsiness detection system, leveraging image processing techniques and advanced fuzzy logic methods. It also introduces improvements to the Viola‐Jones algorithm for swift and precise driver face, eye, and mouth identification. Extensive experiments involving diverse individuals and scenarios were conducted to assess the system's performance in detecting eye and mouth states. The results are highly promising, with eye detection accuracy at 91.8% and mouth detection achieving a remarkable 94.6%, surpassing existing methods. Real‐time testing in varied conditions, including day and night scenarios and subjects with and without glasses, demonstrated the system's robustness, yielding a 97.5% test accuracy in driver drowsiness detection.