Intelligent driver drowsiness detection through fusion of yawning and eye closure

Driver drowsiness is a major factor in most driving accidents. In this paper we present a robust and intelligent scheme for driver drowsiness detection employing the fusion of eye closure and yawning detection methods. In this approach, the driver's facial appearance is captured via a camera installed in the car. In the first step, the face region is detected and tracked in the captured video sequence utilizing computer vision techniques. Next, the eye and mouth areas are extracted from the face; and they are studied to find signs of driver fatigue. Finally, in a fusion phase the driver state is determined and a warning message is sent to the driver if the drowsiness is detected. Our experiments prove the high efficiency of the proposed idea.

[1]  Mehmet Celenk,et al.  Prediction of driver head movement via Bayesian Learning and ARMA modeling , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[2]  Dot Hs,et al.  The 100 Car Naturalistic Driving Study , 2002 .

[3]  Heidi D. Howarth,et al.  An Evaluation of Emerging Driver Fatigue Detection Measures and Technologies , 2009 .

[4]  Tzyy-Ping Jung,et al.  An EEG-based subject- and session-independent drowsiness detection , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[5]  Bo Cheng,et al.  An on-board system for detecting driver drowsiness based on multi-sensor data fusion using Dempster-Shafer theory , 2009, 2009 International Conference on Networking, Sensing and Control.

[6]  Aurobinda Routray,et al.  A novel drowsiness detection scheme based on speech analysis with validation using simultaneous EEG recordings , 2010, 2010 IEEE International Conference on Automation Science and Engineering.

[7]  Sharath Pankanti,et al.  Biometrics: a tool for information security , 2006, IEEE Transactions on Information Forensics and Security.

[8]  Claudio A. Perez,et al.  Face and eye tracking algorithm based on digital image processing , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[9]  村田 幸治,et al.  Noninvasive biological sensor system for detection of drunk driving , 2012 .

[10]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[11]  Erik Hjelmås,et al.  Face Detection: A Survey , 2001, Comput. Vis. Image Underst..

[12]  Noelia Hernández,et al.  Vision-based drowsiness detector for a realistic driving simulator , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[13]  Miguel Ángel Sotelo,et al.  Real-time system for monitoring driver vigilance , 2004, Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005..

[14]  Huabiao Qin,et al.  An Improved Real Time Eye State Identification System in Driver Drowsiness Detection , 2007, 2007 IEEE International Conference on Control and Automation.

[15]  N. A. Abdul Rahim,et al.  RGB-H-CbCr skin colour model for human face detection , 2006 .

[16]  Arun Ross,et al.  An introduction to biometric recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  Chih-Sheng Hsu,et al.  Irregular Vehicle Behavior Warning Modules , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[18]  Richard D. Jones,et al.  Automated video-based measurement of eye closure for detecting behavioral microsleep , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[19]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..