Long Short-term Memory Network Based Fatigue Detection with Sequential Mouth Feature

Fatigue driving detection helps reduce the occurrence of traffic accidents. Currently, the fatigue detection methods still have following challenges: 1) In the visual fatigue detection system, face detection is the foundation of fatigue detection and it is always susceptible to changes of illumination and facial expression, which lowers the accuracy of face detection. 2) It is so difficult to distinguish some behaviors of a driver such as singing and yawning in a single frame that making false fatigue detection in the visual fatigue detection system. To overcome the problems mentioned above, a fatigue detection method based on sequential mouth feature with long short-term memory network is proposed. The proposed method mainly consists of two parts: locating of facial landmark based on face and mouth detection, and yawning detection based on residual network (Resnet) and long short-term memory (LSTM). The face detection method is based on Histogram of Oriented Gradient (HOG) feature and Support Vector Machine (SVM) classifier, which is robust to the changes of illumination and slightly facial expression. The mouth detection is based on cascaded regression tree, which extracts the mouth region from the detected face. The Resnet and LSTM based yawning detection extracts spatial-temporal features from the mouth regions between frames and distinguishes the behaviors about yawning to identify fatigue of a driver with sequential mouth feature. The proposed method achieves accuracy of 94.9% on the YawDD dataset, which outperforms the compared methods. Experimental results demonstrate that the proposed method satisfies the performance of fatigue detection.