Abstract
A non-intrusive fatigue detection method based on fast facial feature analysis is proposed in this paper. Firstly, the facial landmarks are obtained by the supervised descent method, which automatically tracks the faces and fits the facial appearance very fast and accurately. It covers facial landmarks over a wide range of human head rotations. Then the aspect ratios of eyes and mouth are computed with the coordinates of the detected facial feature points. We interpolate and smooth those aspect ratios by a forgetting factor to deal with the occasionally missing detection of facial features. Thirdly, the degrees of eye closure and mouth opening are evaluated with two Gaussian based membership functions. Finally, the driver fatigue state is inferred by several IF-Then logical relationships by evaluating the duration of eye closure and mouth opening. Experiments are conducted on 41 videos to show the effectiveness of the proposed method.
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References
Takei, Y., Furukawa, Y.: Estimate of driver’s fatigue through steering motion. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 1765–1770 (2005)
Hostens, I., Ramon, H.: Assessment of muscle fatigue in low level monotonous task performance during car driving. J. Electromyogr. Kinesiol. 15(3), 266–274 (2005)
King, L.M, Nguyen, H.T, Lal, S.K.L.: Early driver fatigue detection from electroencephalography signals using artificial neural networks. In: 28th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, pp. 2187–2190 (2006)
Jung, S.-J., Shin, H.-S., Cshung, W.-Y.: Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel. IET Intel. Transport Syst. 8(1), 43–50 (2014)
Nambiar, V.P., Khalil-Hani, M., Sia, C.W., Marsono, M.N.: Evolvable block-based neural networks for classification of driver drowsiness based on heart rate variability. IEEE International Conference on Circuits and Systems (ICCAS), pp. 156–161(2012)
Ebrahim P., Stolzmann W., Yang, B.: Eye movement detection for assessing driver drowsiness by electrooculography. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4142–4148 (2013)
Murugappan, M., Wali, M.K., Ahmmad, R.B., Murugappan, S.: Subtractive fuzzy classifier based driver drowsiness levels classification using EEG. In: International Conference on Communications and Signal Processing (ICCSP), pp. 159–164 (2013)
Batista, J.: A drowsiness and point of attention monitoring system for driver vigilance. In: IEEE Conference on Intelligent Transportation Systems, pp. 702–708 (2007)
Wahlstrom E, Masoud O, Papanikolopoulos, N.: Vision-based methods for driver monitoring. In: IEEE Conference on Intelligent Transportation Systems, vol. 2, pp. 903- 908(2003)
Hong T, Qin, H.: Drivers drowsiness detection in embedded system. In: IEEE International Conference on Vehicular Electronics and Safety, pp. 1–5 (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–518, Hawaii, USA (2001)
Singh, H., Bhatia, J.S., Kaur, J.: Eye tracking based driver fatigue monitoring and warning system. In: 2010 India International Conference on Power Electronics (IICPE), pp. 1–6 (2011)
Coetzer, R.C., Hancke, G.P.: Eye detection for a real-time vehicle driver fatigue monitoring system. In: IEEE Intelligent Vehicles Symposium (IV), pp. 66–71 (2011)
Zhang, S., Liu, F., Li, Z.: An effective driver fatigue monitoring system. International Conference on Machine Vision and Human-Machine Interface, pp. 279–282 (2010)
Zhang, W., Cheng, B., Lin, Y.: Driver drowsiness recognition based on computer vision technology. Tsinghua Sci. Technol. 17(3), 354–362 (2012)
Tadesse E., Sheng, W., Liu, M.: Driver drowsiness detection through HMM based dynamic modeling. In: IEEE International Conference on Robotics and Automation, pp. 4003–4008 (2014)
Ling, Z., Lu, X., Wang, Y., Zhou, Y., Wang, G., Li, J.: Local sparse representation for driver drowsiness expression recognition. In: Chinese Automation Congress (CAC), pp. 733–737 (2013)
Anumas S., Kim, S-.C.: Driver fatigue monitoring system using video face images & physiological information. In: International Conference on Biomedical Engineering, pp. 125–130 (2011)
Xiong, X., De la Torre, F.: Supervised descent method and its applications to face alignment. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 532–539, Portland, OR (2013)
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Zheng, R., Tian, C., Li, H., Li, M., Wei, W. (2015). Fatigue Detection Based on Fast Facial Feature Analysis. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9315. Springer, Cham. https://doi.org/10.1007/978-3-319-24078-7_48
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DOI: https://doi.org/10.1007/978-3-319-24078-7_48
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