Fatigue Driving Detection Based on Facial Features

In recent years, the incidence of traffic accidents has grown rapidly, which has brought great threats to people's lives and property. At the same time, fatigue driving detection and early warning systems have become a new research hotspot. The traditional fatigue detection based on facial features is dependent on digital image processing too much. Judging the feature state requires a lot of image processing work and additional complex algorithm support, such as ellipse fitting. When comes to judging whether driver wear glasses, the workload will increase greatly, and the results of the final test are unsatisfactory, which also makes the image processing-based fatigue detection method not recognized by some researchers. This paper introduces a method based on convolutional neural network for facial feature extraction and state determination. The fatigue state is judged by blending the mouth state feature and the eye state feature, which effectively improves the accuracy and robustness of the judgment result. Depending on the establishment of the facial key point model, the efficiency of the mouth state judgment is greatly improved, and the problem of the driver wearing the glasses is also cleverly avoided. The experimental results show that the method can accurately and efficiently complete the fatigue detection work, and the performance is better than other image processing-based detection methods.