A lightweight fatigue driving detection method based on facial features-Reference-Cited by-同舟云学术

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A lightweight fatigue driving detection method based on facial features

Published:2024-04-12 Issue:S1 Volume:18 Page:335-343
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Zhu Jun-Wei,Ma Yan-E,Xia Jia,Zhou Xiao-Gang

Funder

the Key Research and Development Program of Zhejiang

the National Natural Science Foundation of China

the Zhejiang Provincial Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11760-024-03156-7.pdf

Reference39 articles.

1. Global Status Report on Road Safety 2018. World Health Organization, Geneva (2018), Licence: CC BYNC-SA 3.0 IGO

2. MacLean, A.W.: Sleep and driving. In: Handbook of Behavioral Neuroscience, pp. 611–622 (2019)

3. Tuncer, T., Dogan, S., Subasi, A.: EEG-based driving fatigue detection using multilevel feature extraction and iterative hybrid feature selection. Biomed. Signal Process. Control 68(2), 102591 (2021)

4. Wang, F., Wu, S., Ping, J., Xu, Z., Chu, H.: EEG driving fatigue detection with PDC-based brain functional network. In: IEEE Sensors Journal, pp. 10811–10823 (2021)

5. Chen, S., Xu, K., Yao, X., Ge, J., Li, L., Zhu, S., Li, Z.: Information fusion and multi-classifier system for miner fatigue recognition in plateau environments based on electrocardiography and electromyography signals. Comput. Methods Programs Biomed. 211, 106451 (2021)

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