A Driver Drowsiness Detection Scheme Based on 3D Convolutional Neural Networks

It is an obvious fact that drivers’ drowsiness is more likely to cause traffic accidents. Recently, driver drowsiness detection has drawn considerable attention. In this paper, a novel drowsiness detection scheme is proposed, which can recognize drivers’ drowsiness actions through their facial expressions. First, a drowsiness action recognition model based on 3D-CNN is proposed, which can effectively distinguish drivers’ drowsiness actions and nondrowsiness actions. Second, a fusion algorithm of the two input streams is proposed, which can fuse gray image sequence and optical image sequence containing target motion information. Finally, the proposed model is evaluated on National Tsinghua University Driver Drowsiness Detection (NTHU-DDD) dataset. The experimental results show that the algorithm performs better than other algorithms, and its accuracy reaches 86.64%.