Fast Driver Distraction and Fatigue Detection Systems based on Multiple Facial Behavior

Driver distraction and fatigue detection systems can effectively reduce car accidents and ensure the safety of traffic participants. Most of the existing vision-based approaches use facial landmarks as driver’s states. However, facial landmark detection is inaccurate under the large angle of head posture, which impacts the accuracy of further processing. This paper presents an effective method using convolution neural networks (CNNs). The method firstly deploys a modified MTCNN to detect the face region. Then, a lightweight multi-task CNN is proposed to detect eye regions, mouth landmarks and 3D head pose, and a simple CNN is used to detect eye closure independently. An angle-adapted loss function is applied to improve the landmark detection accuracy under the large posture. Finally, multiple abnormal behaviors are recognized to determine distraction and fatigue driving. Experiments show that our proposed method is superior to existing methods in both accuracy and running speed.