Accurate Fatigue Detection Based on Multiple Facial Morphological Features
Joint Authors
Li, Kangning
Wang, Shangshang
Du, Chang
Huang, Yuxin
Feng, Xin
Zhou, Fengfeng
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-28
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Fatigue driving is becoming a dangerous and common situation for drivers and represents a significant factor for fatal car crashes.
Machine learning researchers utilized various sources of information to detect driver’s drowsiness.
This study integrated the morphological features of both the eye and mouth regions and extensively investigated the fatigue detection problem from the aspects of feature numbers, classifiers, and modeling parameters.
The proposed algorithm REcognizing the Drowsy Expression (REDE) achieved the 10-fold cross-validation accuracy 96.07% and took about 21 milliseconds to process one image.
REDE outperformed the existing four studies on both fatigue detection accuracy and running time and is fast enough to handle the task of real-time fatigue monitoring captured at the rate of 30 frames per second.
To further facilitate the research of fatigue detection, the raw data and the feature matrix were also released.
American Psychological Association (APA)
Li, Kangning& Wang, Shangshang& Du, Chang& Huang, Yuxin& Feng, Xin& Zhou, Fengfeng. 2019. Accurate Fatigue Detection Based on Multiple Facial Morphological Features. Journal of Sensors،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1191625
Modern Language Association (MLA)
Li, Kangning…[et al.]. Accurate Fatigue Detection Based on Multiple Facial Morphological Features. Journal of Sensors No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1191625
American Medical Association (AMA)
Li, Kangning& Wang, Shangshang& Du, Chang& Huang, Yuxin& Feng, Xin& Zhou, Fengfeng. Accurate Fatigue Detection Based on Multiple Facial Morphological Features. Journal of Sensors. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1191625
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1191625