Detection of fatigue driving behavior based on facial expression

The scene environment in the cab is complex and changeable, which is easy to be disturbed by the changes of weather, light and other conditions, which brings many difficulties to the driver’s fatigue driving detection. This paper mainly analyzes the specific characteristics of the driving indoor environment, uses some technologies in the field of computer vision and image processing to improve the image quality, uses the Adaboost face detection algorithm based on Haar-like features, to detect and locate the driver’s face in the cockpit, and reduces the face detection area to determine whether the driver looks left and right or bowed. Then, on the basis of detecting the face, the method of gray scale integral projection is adopted to locate the eyes and the mouth, and the Bezier curve is used to fit the contour of the eyes and the mouth, and to judge the driver’s eye opening and the mouth opening by comprehensively analyzing the vertical and horizontal ratio. Later, ocular fatigue analysis was performed by using the PERCLOS(Percentage of Eyelid Closure over the Pupil over Time) criteria and by eyeblink frequency, and mouth fatigue analysis was also performed by the frequency of yawning.