Driver Drowsiness Detection by Multi-task and Transfer Learning

In this busy modern society, there are many external and psychological factors that can cause people to feel tired. The severity of fatigue driving is comparable to drunk driving when we consider the accident rate. Therefore, how to avoid this situation has become an important issue. With the trend of machine learning becoming more mature, facial expression recognition has been widely used in real life. A large number of studies and reports about fatigue driving detection and how to improve fatigue driving can be found. Most of them either detect drowsiness states without detailed facial expressions or just look at a single part of face such as eye or mouth. However, we consider that each facial feature is highly correlated. For example, when a driver gets tired, his/her mouth and eyes are thought to change the states together. Thus, it is important to evaluate more than one facial feature at a time. Therefore, in this paper, we propose a new driver-drowsiness detection method by using multi-task and transfer learning. The proposed method first captures the drivers’ facial areas frame-by-frame in videos, and learns different facial features synchronously. The experimental results show that the proposal outperforms the ever-proposed methods on four scenarios out of the five and on the average in the NTHU driver drowsiness detection video dataset.