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.

[1]  Wang Rongben,et al.  Monitoring mouth movement for driver fatigue or distraction with one camera , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[2]  Pengfei Shi,et al.  Yawning detection for determining driver drowsiness , 2005, Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, 2005..

[3]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, CAIP.

[4]  Zhiwei Zhu,et al.  Real-time nonintrusive monitoring and prediction of driver fatigue , 2004, IEEE Transactions on Vehicular Technology.

[5]  Qiang Ji,et al.  A probabilistic framework for modeling and real-time monitoring human fatigue , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[6]  A Williamson,et al.  Review of on-road driver fatigue monitoring devices , 2005 .

[7]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Paul Stephen Rau,et al.  Drowsy Driver Detection and Warning System for Commercial Vehicle Drivers: Field Operational Test Design, Data Analyses, and Progress , 2005 .

[9]  Kongqiao Wang,et al.  A hierarchical multiscale and multiangle system for human face detection in a complex background using gravity-center template , 1999, Pattern Recognit..