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Facial Fatigue Detection Based on Machine Learning

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Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 517))

Abstract

One of the most important reasons for productivity decline and accidents is work fatigue. Work fatigue research has become more and more important in modern society. This paper proposes a method to detect fatigue, Build new features, propose new compensation methods, and combine the existing models to make the method adapt to the complex environment. As a result, it effectively improves the work fatigue detection efficiency and accuracy under the production environment.

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References

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Acknowledgement

This project is supported by the Project of Intelligent Manufacturing Integrated Standardization and New Model Application.

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Authors and Affiliations

  1. Key Laboratory of Universal Wireless Communications, MOE Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Beijing, China

    Dewei Zheng & Chenglin Zhao

  2. China Petroleum Technology & Development Corporation, Beijing, China

    Shaohua Cui

Authors
  1. Dewei Zheng
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  2. Shaohua Cui
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  3. Chenglin Zhao
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Corresponding author

Correspondence to Dewei Zheng .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Cite this paper

Zheng, D., Cui, S., Zhao, C. (2020). Facial Fatigue Detection Based on Machine Learning. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-13-6508-9_20

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  • DOI: https://doi.org/10.1007/978-981-13-6508-9_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6507-2

  • Online ISBN: 978-981-13-6508-9

  • eBook Packages: EngineeringEngineering (R0)

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