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Driver Yawning Detection Based on Subtle Facial Action Recognition.

Yang, H., Liu, L., Min, W., Yang, X. and Xiong, X., 2021. Driver Yawning Detection Based on Subtle Facial Action Recognition. IEEE Transactions on Multimedia, 23, 572 - 583.

Full text available as:

Driver+Yawning+DetectionFeb.25,2019YangHaoRevised.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1109/TMM.2020.2985536


Various investigations have shown that driver fatigue is the main cause of traffic accidents. Research on the use of computer vision techniques to detect signs of fatigue from facial actions, such as yawning, has demonstrated good potential. However, accurate and robust detection of yawning is difficult because of the complicated facial actions and expressions of drivers in the real driving environment. Several facial actions and expressions have the same mouth deformation as yawning. Thus, a novel approach to detecting yawning based on subtle facial action recognition is proposed in this study to alleviate the abovementioned problems. A 3D deep learning network with a low time sampling characteristic is proposed for subtle facial action recognition. This network uses 3D convolutional and bidirectional long short-term memory networks for spatiotemporal feature extraction and adopts SoftMax for classification. A keyframe selection algorithm is designed to select the most representative frame sequence from subtle facial actions. This algorithm rapidly eliminates redundant frames using image histograms with low computation cost and detects outliers by median absolute deviation. A series of experiments are also conducted on YawDD benchmark and self-collected datasets. Compared with several state-of-the-art methods, the proposed method has high yawning detection rates and can effectively distinguish yawning from similar facial actions.

Item Type:Article
Additional Information:This work was supported by the National Natural Science Foundation of China under Grant 61762061 and Grant 61603256and in part by the Natural Science Foundation of Jiangxi Province, China under Grant 20161ACB20004.
Uncontrolled Keywords:driver fatigue; yawning detection; keyframe selection; subtle facial action; 3D deep learning network
Group:Faculty of Media & Communication
ID Code:35213
Deposited By: Symplectic RT2
Deposited On:23 Feb 2021 08:12
Last Modified:14 Mar 2022 14:26


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