Eye Location Based on Structure Feature for Driver Fatigue Monitoring
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:52328", author = "Qiong Wang", title = "Eye Location Based on Structure Feature for Driver Fatigue Monitoring", 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", volume = "4", number = "12", pages = "1857-5", }