Random Articles
Reseach Article
Morphology based Facial Feature Extraction and Facial Expression Recognition for Driver Vigilance
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 51 - Number 2 |
| Year of Publication: 2012 |
| Authors: K. S. Chidanand Kumar |
10.5120/8014-1142
|
K. S. Chidanand Kumar . Morphology based Facial Feature Extraction and Facial Expression Recognition for Driver Vigilance. International Journal of Computer Applications. 51, 2 ( August 2012), 17-24. DOI=10.5120/8014-1142
Abstract
Driver fatigue is one of the leading causes of traffic accidents. Therefore, the use of assistive systems that monitor a driver’s level of vigilance and alert the driver in case of drowsiness and distraction can be significant in the prevention of accidents. This paper presents morphology based operations in extracting various visual cues like eye, eye brows, mouth and head movement. The parameters used for detecting fatigue are: eye closure duration measured through eye state information, head movement through orientation of head ellipse and yawning analyzed through mouth state information. This system was validated with synthetic data under real-life fatigue conditions with human subjects of different ethnic backgrounds, genders, and ages; and under different illumination conditions. It was found to be reasonably robust, reliable, and accurate in fatigue characterization.
References
- M. R. Rosekind, E. L. Co, K. B. Gregory, and D. L. Miller, “Crew factors in flight operations XIII: a survey of fatigue factors in corporate/executive aviation operations,” NASA, Ames Research Center, NASA/TM-2000-209 610 (2000).
- Awake Consortium (IST 2000-28062), “System for effective assessment of driver vigilance and warning according to traffic risk estimation (AWAKE),” September 2001–2004. http://www.awake-eu.org (April 16, 2011).
- K. Yammamoto and S. Higuchi, “Development of a drowsiness warning system,” J. Soc. Automotive Eng. Japan, vol. 46, no. 9, pp. 127–133, 1992.
- S. Saito, “Does fatigue exist in a quantitative of eye movement?” Ergonomics, vol. 35, pp. 607–615, 1992.
- Appl. Sci. Lab., “PERCLOS and eye tracking: Challenge and opportunity”, Tech. Rep., Appl. Sci. Lab., Bedford, MA, 1999.
- H. Ueno, M. Kaneda, and M. Tsukino, “Development of drowsiness detection system,” in Proc. Vehicle Navigation Information Systems Conf.,Yokohama, Japan, Aug. 1994, pp. 15–20.
- S. Boverie, J. M. Leqellec, and A. Hirl, “Intelligent systems for video monitoring of vehicle cockpit,” in Proc. Int. Congr. Expo. ITS: Advanced Controls Vehicle Navigation Systems, 1998, pp. 1–5.
- T. D’Orazio, M. Leo, C. Guaragnella, and A. Distante, “A visual approach for driver inattention detection,” Pattern Recogn. 40, 2341–2355 (2007).
- M. Saradadevi and P. Bajaj, “Driver fatigue detection using mouth and yawning analysis”, Int. J. Comput. Sci. Netw. Security 8(6), 183–188 (2008).
- A. Hattori, S. Tokoro, M. Miyashita, I. Tanakam, K. Ohue, and S. Uozumi, “Development of forward collision warning system using the driver behavioral information,” presented at 2006 SAEWorld Congress, Detroit, Michigan (2006).
- E. Murphy-Chutorian,A. Doshi, and M. M. Trivedi, “Head pose estimation for driver assistance systems: a robust algorithm and experimental evaluation,” in Proc. of 10th Int. IEEE Conf. Intelligent Transportation Systems, pp. 709–714 (2007).
- J. Y. Kaminski, D. Knaan, and A. Shavit, “Single image face orientation and gaze detection,” Mach. Vis. Appl. 21, 85–98 (2009).
- D. F. Dinges, M. Mallis, G. Maislin, and J. W. Powell, “Evaluation of techniques for ocular measurement as an index of fatigue and the basis for alertness management,” Department of Transportation Highway Safety Publication 808 762, April (1998).
- P.Viola and M.Jones, “Robust real-time object detection”, International Journal of Computer Vision, 2002, 1(2).
- Otsu, N, “A threshold selection method from gray-level histograms”, IEEE Trans. Systems, Man, and Cybernetics, 9(1), pp. 62-66, 1979.
- Sungji Han, Youngjoon Han and Hernsoo Hahn, "Vehicle Detection Method using Haar-like Feature on Real Time System",World Academy of Science, Engineering and Technology 59 2009.
- Dorin Comaniciu, Visvanathan Ramesh, Peter Meer, “Kernel-Based Object tracking”, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.25 n.5, p.564-575, 2003.
- Michael Mason, Zoran Duric,"Using Histograms to Detect and Track Objects in Color Video", AIPR pp. 154-162, 2001.
- L. Harley, T. Horberry, N. Mabbott, and G. Krueger, “Review of fatigue detection and prediction technologies,” National Road Transport Commission, (2000).
- Q. Ji and X. Yang, “Real-time eye, gaze, and face pose tracking for monitoring driver vigilance,” Real-Time Imaging 8(5), 357–377 (2002).
- D. F. Dinges, M. M. Mallis, G. Maislin, and J. W. Powell, “Evaluation of techniques for ocular measurement as an index of fatigue and the basis for alertness management,” U.S. Department of Transportation: National Highway Traffic Safety Administration, DOT HS 808 762 (1998).
- P.Viola and M. Jones. ”Rapid object detection using a boosted cascade of simple features.” In IEEE Conference on Computer Vision and Pattern Recognition 2001, 2001.
- M. Pilu, A. Fitzgibbon, and R. Fisher, "Ellipse-specific direct least-square fitting", IEEE Conference on Image Processing, Lausanne, 1996, vol. 3, pp. 599-602. .
Index Terms
Keywords