@inproceedings{4cf4d4b99be8433faef296a564ddf8e7,
title = "Fatigue detection based on fast facial feature analysis",
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.",
keywords = "Driver drowsiness, Facial feature localization, Fatigue monitoring",
author = "Ruijiao Zheng and Chunna Tian and Haiyang Li and Minglangjun Li and Wei Wei",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 16th Pacific-Rim Conference on Multimedia, PCM 2015 ; Conference date: 16-09-2015 Through 18-09-2015",
year = "2015",
doi = "10.1007/978-3-319-24078-7_48",
language = "英语",
isbn = "9783319240770",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "477--487",
editor = "Yo-Sung Ho and Ro, {Yong Man} and Junmo Kim and Fei Wu and Jitao Sang",
booktitle = "Advances in Multimedia Information Processing – PCM 2015 - 16th Pacific-Rim Conference on Multimedia, Proceedings",
}