research-article

Application of Face Recognition Based on CNN in Fatigue Driving Detection

Authors:

Jun LiAuthors Info & Claims

AIAM 2019: Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing

Article No.: 56, Pages 1 - 5

https://doi.org/10.1145/3358331.3358387

Published: 17 October 2019 Publication History

Abstract

Fatigue driving detection technology based on the external characteristics of the driver has made some progress in many aspects, but the method of driver facial feature extraction needs to be further improved, and the driver's eye location takes a long time, which affects the system recognition rate. The paper uses the fatigue driving detection method to achieve better results than the traditional detection method. The authors applied the convolutional neural network to face recognition, and improve the pupil localization algorithm, effectively overcoming the problem of large calculation of the original algorithm. According to the characteristics of driver's eyes with different width and height ratios in different states, a simple and feasible method of eye state judgment is realized, and the driver's fatigue state is judged by PERCLOS algorithm. The convolutional neural network model is applied to ORL face database, and the face recognition rate is 85%. The improved Hough transform method has a positioning accuracy of 92% for the driver's eyes, respectively, and the recognition rate for the driver's eye state is 83.9%. The authors designed the prototype system of fatigue driving detection based on face recognition which realizes the functions of driver's face feature detection, eye location, eye state judgment and fatigue judgment. The experimental results show that the recognition rate of fatigue is 87.5%.

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Cited By

Hou JZhang BZhong YHe W(2025)Research Progress of Dangerous Driving Behavior Recognition Methods Based on Deep LearningWorld Electric Vehicle Journal10.3390/wevj1602006216:2(62)Online publication date: 21-Jan-2025
https://doi.org/10.3390/wevj16020062
Nemavhola AChibaya CViriri S(2025)A Systematic Review of CNN Architectures, Databases, Performance Metrics, and Applications in Face RecognitionInformation10.3390/info1602010716:2(107)Online publication date: 5-Feb-2025
https://doi.org/10.3390/info16020107
Kong LXie KNiu KHe JZhang W(2024)Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal FusionSensors10.3390/s2402045524:2(455)Online publication date: 11-Jan-2024
https://doi.org/10.3390/s24020455
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Index Terms

Application of Face Recognition Based on CNN in Fatigue Driving Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding

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cover image ACM Other conferences

AIAM 2019: Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing

October 2019

418 pages

ISBN:9781450372022

DOI:10.1145/3358331

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 17 October 2019

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AIAM 2019

AIAM 2019: 2019 International Conference on Artificial Intelligence and Advanced Manufacturing

October 17 - 19, 2019

Dublin, Ireland

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Cited By

Hou JZhang BZhong YHe W(2025)Research Progress of Dangerous Driving Behavior Recognition Methods Based on Deep LearningWorld Electric Vehicle Journal10.3390/wevj1602006216:2(62)Online publication date: 21-Jan-2025
https://doi.org/10.3390/wevj16020062
Nemavhola AChibaya CViriri S(2025)A Systematic Review of CNN Architectures, Databases, Performance Metrics, and Applications in Face RecognitionInformation10.3390/info1602010716:2(107)Online publication date: 5-Feb-2025
https://doi.org/10.3390/info16020107
Kong LXie KNiu KHe JZhang W(2024)Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal FusionSensors10.3390/s2402045524:2(455)Online publication date: 11-Jan-2024
https://doi.org/10.3390/s24020455
Cai JZhang JSun ECai J(2023)A Residual Neural Network-Based Incremental Learning Model for Driver State Perception2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)10.1109/ICECAI58670.2023.10176412(382-386)Online publication date: 12-May-2023
https://doi.org/10.1109/ICECAI58670.2023.10176412
Janfaza VWeston KRazavi MMandal SMahmud FHilty AMuzahid A(2023)MERCURY: Accelerating DNN Training By Exploiting Input Similarity2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA56546.2023.10071051(638-650)Online publication date: Feb-2023
https://doi.org/10.1109/HPCA56546.2023.10071051
Peng BZhang YWang MChen JGao D(2023)T-A-MFFNet: Multi-feature fusion network for EEG analysis and driving fatigue detection based on time domain network and attention networkComputational Biology and Chemistry10.1016/j.compbiolchem.2023.107863104(107863)Online publication date: Jun-2023
https://doi.org/10.1016/j.compbiolchem.2023.107863
Kaur JSingh W(2023)A systematic review of object detection from images using deep learningMultimedia Tools and Applications10.1007/s11042-023-15981-y83:4(12253-12338)Online publication date: 24-Jun-2023
https://doi.org/10.1007/s11042-023-15981-y
AKSOY BÖZSOY KYÜCEL MEKREM ÖSALMAN O(2022)Motion Control of the Robot Arm Manufactured with a Three-Dimensional Printer and Hardness Detection of ObjectsYapay Zekâya Dayalı Robot Kol ile Hareket ve Farklı Nesnelerin Sertlik KontrolüBilişim Teknolojileri Dergisi10.17671/gazibtd.105937815:3(289-300)Online publication date: 31-Jul-2022
https://doi.org/10.17671/gazibtd.1059378
Zhao DZhong YFu ZHou JZhao M(2022)A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor InformationJournal of Advanced Transportation10.1155/2022/72875112022(1-16)Online publication date: 7-Oct-2022
https://doi.org/10.1155/2022/7287511
Zeng XWang FWang BWu CLiu KAu O(2022)In-Vehicle Sensing for Smart CarsIEEE Open Journal of Vehicular Technology10.1109/OJVT.2022.31745463(221-242)Online publication date: 2022
https://doi.org/10.1109/OJVT.2022.3174546
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