presented a night monitoring system for real-time fatigue driving detection, 12 but the monitoring system requires additional infrared lighting equipment, which is limited to some specific applications. The following is a brief introduction to some of the algorithms for detecting fatigue by facial feature analysis. This method is noninvasive and easy to implement and is applied to the fatigue detection in this paper. PERCLOS is the abbreviation of percentage of eyelid closure over the pupil over time, which is the percentage of the closing time of the eye over a specific period of time. The third method analyzes the driver’s face, such as the PERCLOS value, blink frequency, head posture, and yawn detection. The second method is used to measure the behaviors of vehicles, 7 – 11 such as speed, steering wheel rotation angle, and lane departure detection however, this method is affected by driving conditions, driving experience, and vehicle type. However, these methods are invasive and require contact with the driver’s body. The first method measures the driver’s physiological parameters 2 – 6 by using tools such as electroencephalogram and electrocardiogram. Researchers have proposed many fatigue detection methods to solve this problem, which can be divided into three types: 1 physiological parameters, vehicle behaviors, and facial feature analysis.
In recent years, driver fatigue has become one of the most important factors for traffic accidents, which has come at a great cost to the safety and property of drivers and pedestrians. This paper is based on the three different databases to evaluate the performance of the proposed algorithm, and it does not need training with high calculation efficiency. Based on the color difference of the lip, skin, and internal mouth, the internal mouth contour can be fitted to analyze the opening state of mouth at the same time, another unique and effective yawning judgment mechanism is considered to determine whether the driver is tired. Meanwhile, the value of chromatism s is defined in the RGB space, and the mouth is accurately located through lip segmentation. In addition, an effective algorithm is proposed to solve the problem of contour fitting when the human eye is affected by strabismus. Then, the eyes are located by an EyeMap algorithm through a clustering method to extract the sclera-fitting eye contour and calculate the contour aspect ratio. First, the face area is detected in the acquired image database. An algorithm that analyzes the state of the eye and mouth by extracting contour features is proposed. Eye and mouth state analysis is an important step in fatigue detection.