Multi‐parameter fusion driver fatigue detection method based on facial fatigue features

Abstract Fatigued driving is one of the main causes of traffic accidents. In order to improve the detection speed of fatigue driving recognition, this paper proposes a driver fatigue detection method based on multi‐parameter fusion of facial features. It uses a cascaded Adaboost object classifier to detect faces in video streams. The DliB library is employed for facial key point detection, which locates the driver's eyes and mouth to determine their states. The eye aspect ratio (EAR) is calculated to detect eye closure, and the mouth aspect ratio (MAR) is calculated to detect yawning frequency and count. The detected percentage of eye closure (PERCLOS) value is combined with yawning frequency and count, and a multi‐feature fusion approach is used for fatigue detection. Experimental results show that the accuracy of blink detection is 91% and the accuracy of yawn detection is 96.43%. Furthermore, compared to the models mentioned in the comparative experiments, this model achieves two to four times faster detection times while maintaining accuracy.