Commenced in January 2007
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Edition: International
Paper Count: 30174
Eye Location Based on Structure Feature for Driver Fatigue Monitoring

Authors: Qiong Wang

Abstract:

One of the most important problems to solve is eye location for a driver fatigue monitoring system. This paper presents an efficient method to achieve fast and accurate eye location in grey level images obtained in the real-word driving conditions. The structure of eye region is used as a robust cue to find possible eye pairs. Candidates of eye pair at different scales are selected by finding regions which roughly match with the binary eye pair template. To obtain real one, all the eye pair candidates are then verified by using support vector machines. Finally, eyes are precisely located by using binary vertical projection and eye classifier in eye pair images. The proposed method is robust to deal with illumination changes, moderate rotations, glasses wearing and different eye states. Experimental results demonstrate its effectiveness.

Keywords: eye location, structure feature, driver fatiguemonitoring

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058697

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References:


[1] S.Y. Zhao, and R.R. Grigat, "Robust eye detection under active infrared illumination," Proceedings of International Conference on Pattern Recognition, vol.4, pp. 481-484, 2006.
[2] H. Huang, Y.S. Zhou, and et al., "An optimal eye locating and tracking systems for driver fatigue monitoring", Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, pp. 1144 -1148, 2007.
[3] W. Di, R.B. Wang, and et al., "Driver eye feature extraction based on Infrared illumination", Proceedings of IEEE Intelligent Vehicles Symposium, pp.330-334, pp. 2009.
[4] R Hsu, M. Mottleb, and A. K. Jain, "Face detection in color images", IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 4, No. 5, pp. 696-706, 2002.
[5] H. A. Rowley, S. Baluja, and T. Kanade, "Neural network-based face detection," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 20, No. 1, pp 23-38, 1998.
[6] P.Viola, M.J.Jones, "Robust real-time face detection", International Journal of Computer Vision. Vol.57, No.2, pp.137-154, 2004.
[7] T. Kawaguchi, and M. Rizon, "Iris detection using intensity and edge information," Pattern Recognition. vol. 36, pp.549-562, 2003.
[8] A. Tauseef, and K. Intaek, "An improved eye location algorithm using multi-cue facial Information", Proceedings of International Conference on Computer, Control and Communication, pp.1-6, 2009.
[9] JF Ren, and XD. Jiang, "A method for accurate localization of facial features", Proceedings of International Conference on Image Processing, pp.2733-2736, 2009.
[10] Q. Wang, WK Yang, and et al., "Face detection using binary template matching and SVM", LNAI4099, pp.1237-1241, 2006.
[11] J.X Wu, and Z.H Zhou, "Efficient face candidates selector for face Detection," Pattern Recognition, 2003, Vol. 36, pp. 1175-1186.
[12] V. Vapnik, "The nature of statistical learning theory," New York: Springer-Verlag, 1995.