Face Fatigue Feature Detection Based on Improved D-S Model in Complex Scenes

Abstract

Fatigued driving is one of the main causes of road traffic accidents. In the process of fatigued driving detection, the evaluation based on a single sign is biased. To improve the adaptability and accuracy of fatigued driving detection, this paper proposes an improved D-S evidence theory-based algorithm for detecting facial fatigue signs. This algorithm uses the multi-thread-optimized Dlib to track and locate the image of the face, captures the 68 key points of the driver's face with reference to the Dlib open-source library, and narrows the target areas to the eyes and mouth regions. Based on the video stream, it calculates the horizontal and vertical ratios of the eyes and mouth to determine the fatigue sign subsets based on the EAR and MAR within a unit cycle, and calculates the Pitch angle after converting the head pose from 2D images to 3D models, which is used for determining the status of the head pose. The algorithm then fuses multiple feature subsets and uses the improved D-S evidence theory to optimize the weights of the three subsets to mitigate the influence of the "general conflict" and "one veto" problems, and to increase the temporal correlation coefficient of a single fatigue sign. Experimental results show that the improved D-S face detection algorithm can effectively solve the problems of lighting and partial occlusion in complex environments, with an accuracy rate of 93.8% for detecting facial fatigue signs.

Research on Fatigued-Driving Detection Method by Integrating Lightweight YOLOv5s and Facial 3D Keypoints

“…Currently, research on driver-fatigue detection mainly focuses on the field of road traffic and can be divided into three methods: detection based on vehicle driving characteristics [3][4][5], detection based on driver physiological characteristics [6][7][8], and detection based on computer vision of driver facial features [9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Among them, visual-based detection uses cameras or other image sensors to capture the facial-feature changes or headmovement information of the driver.…”

“…It achieved a 94.51% accuracy in real-time video detection. Cai et al [22] used multi-thread optimized Dlib to narrow the face-feature region to the real-time changes of the eyes, mouth, and head and fused multiple feature subsets to realize the fatigued-driving signal detection method based on D-S evidence theory. However, a fatal problem that has been largely overlooked in the above studies is the tendency of the Dlib 2D facial landmark extraction library to lose feature points and have poor real-time performance when there are significant changes in the driver's head pose.…”