IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v11y2019i5p115-d230960.html
   My bibliography  Save this article

Convolutional Two-Stream Network Using Multi-Facial Feature Fusion for Driver Fatigue Detection

Author

Listed:
  • Weihuang Liu

    (School of Software, South China Normal University, Guangzhou 510641, China)

  • Jinhao Qian

    (School of Software, South China Normal University, Guangzhou 510641, China)

  • Zengwei Yao

    (School of Software, South China Normal University, Guangzhou 510641, China)

  • Xintao Jiao

    (School of Software, South China Normal University, Guangzhou 510641, China)

  • Jiahui Pan

    (School of Software, South China Normal University, Guangzhou 510641, China)

Abstract

Road traffic accidents caused by fatigue driving are common causes of human casualties. In this paper, we present a driver fatigue detection algorithm using two-stream network models with multi-facial features. The algorithm consists of four parts: (1) Positioning mouth and eye with multi-task cascaded convolutional neural networks (MTCNNs). (2) Extracting the static features from a partial facial image. (3) Extracting the dynamic features from a partial facial optical flow. (4) Combining both static and dynamic features using a two-stream neural network to make the classification. The main contribution of this paper is the combination of a two-stream network and multi-facial features for driver fatigue detection. Two-stream networks can combine static and dynamic image information, while partial facial images as network inputs can focus on fatigue-related information, which brings better performance. Moreover, we applied gamma correction to enhance image contrast, which can help our method achieve better results, noted by an increased accuracy of 2% in night environments. Finally, an accuracy of 97.06% was achieved on the National Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset.

Suggested Citation

  • Weihuang Liu & Jinhao Qian & Zengwei Yao & Xintao Jiao & Jiahui Pan, 2019. "Convolutional Two-Stream Network Using Multi-Facial Feature Fusion for Driver Fatigue Detection," Future Internet, MDPI, vol. 11(5), pages 1-13, May.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:5:p:115-:d:230960
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/11/5/115/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/11/5/115/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jianliang Min & Ping Wang & Jianfeng Hu, 2017. "Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-19, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sobhan Sheykhivand & Tohid Yousefi Rezaii & Saeed Meshgini & Somaye Makoui & Ali Farzamnia, 2022. "Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing," Sustainability, MDPI, vol. 14(5), pages 1-22, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:11:y:2019:i:5:p:115-:d:230960. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.