Fatigue driving is one of the main causes of traffic accidents. It is important social significance to accurately and effectively detect and prevent the drivers' fatigue driving. Based on the research and comparison of previous work, this study designs a driver fatigue detection mechanism based on machine vision and image processing. First, the continuous frame image (video) is used to perform face detection using AdaBoost algorithm, and the approximate human eye area is segmented according to the distribution features of the human face "three courts and five holes". In the process of human eye positioning, the OSTU threshold segmentation, nonlinear point operations, and integral projections are used to eliminate eyebrows, and three influence factors, namely the fuzzy comprehensive evaluation algorithm for the ratio of the length to the width of the rectangular area of the eye, fitting the area of the ellipse, and the proportion of pupil melanin are analyzed to determine the open or closed state of the eye. Finally, according to the PERCLOS principle, the fatigue state of the driver is detected. The experimental results show that the proposed algorithm can accurately distinguish the open or closed state of the eyes, thus detect the driver's fatigue state with higher accuracy and practicability.