Yawning Detection for Monitoring Driver Fatigue

Fatigue driving is an important reason of traffic accidents. Yawning is an evidence of driver fatigue. This paper proposes to locate and track a driver's mouth movement using a CCD camera to study on monitoring and recognizing a driver's yawning. Firstly detecting drivers' faces uses Gravity-Center template, then detecting drivers' left and right mouth corners by grey projection, and extracting texture features of drivers' mouth corners (left and right) using Gabor wavelets. Finally LDA is applied to classify feature vectors to detect yawning. The method is tested on 400 images from twenty videos. In contrast, yawning is also detected by the ratio of mouth height and width. The experiment results show that Gabor coefficients are more powerful than geometric features to detect yawning and the average recognition rate is 95% which has more than 20% improvement.