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Driver fatigue detection based on deeply-learned facial expression representation

  • Published:2020-08 Issue: Volume:71 Page:102723
  • ISSN:1047-3203
  • Container-title:Journal of Visual Communication and Image Representation
  • language:en
  • Short-container-title:Journal of Visual Communication and Image Representation

Author:

Liu Zhongmin,Peng Yuxi,Hu Wenjin

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Media Technology,Signal Processing

Reference21 articles.

1. Fatigue driving detection method based on eye movement characteristics;Niu;J. Harbin Eng. Univ.,2015

2. Driving fatigue detection based on EEG recognition combined with maneuvering features;Wang;J. Sci. Instrum.,2014

3. A non-contact driving fatigue detection technology based on driver's physiological signal;Wang;Automotive Eng.,2018

4. Driver fatigue state detection based on vehicle maneuvering signals;Li;J. Shanghai Jiaotong Univ.,2010

5. Fatigue driving detection based on Haar feature and extreme learning machine;Zheng;J. China Univ. Posts Telecommun.,2016

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1. Research on fatigue detection of flight trainees based on face EMF feature model combination with PSO-CNN algorithm;Scientific Reports;2024-09-04

2. Facial feature point detection under large range of face deformations;Journal of Visual Communication and Image Representation;2024-08

3. Self-Supervised Multi-Granularity Graph Attention Network for Vision-Based Driver Fatigue Detection;IEEE Transactions on Emerging Topics in Computational Intelligence;2024-08

4. Driver fatigue detection method based on facial image analysis;2024 16th International Conference on Human System Interaction (HSI);2024-07-08

5. Human–Machine Shared Control for Path Following Considering Driver Fatigue Characteristics;IEEE Transactions on Intelligent Transportation Systems;2024-07
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