Discussion on Machine Learning and Deep Learning based Makeup Considered Eye Status Recognition for Driver Drowsiness-Reference-Cited by-同舟云学术

Discussion on Machine Learning and Deep Learning based Makeup Considered Eye Status Recognition for Driver Drowsiness

Published:2019 Issue: Volume:147 Page:264-270
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Nojiri Naoto,Kong Xiangbo,Meng Lin,Shimakawa Hiromitsu

Publisher

Elsevier BV

Subject

General Engineering

Reference14 articles.

1. Oraan Khunpisuth, Taweechai Chotchinasri, Varakorn Koschakosai and Narit Hnoohom. (2016) “Driver Drowsiness Detection using Eye-Closeness Detection.” the 12th Int. Conf. on Signal-Image Technology& Internet-Based Systems 661–668.

2. Kingshuk Mukherjee, Rakesh Karmakar and Souvik Das. (2014) “Effective Estimation of Driver Drowsiness Based on Eye Status Detection and Analysis.” 2014 Int. Conf. on Devices, Circuits and Communications (ICDCCom).

3. Cui Xu, Ying Zheng and Zengfu Wang. (2008) “Efficient Eye States Detection in Real-Time for Drowsy Driving Monitoring System.” IEEE Int. Conf. on Information and Automation 170–174.

4. Ashish Kumar and Rusha Patra, (2018) “Driver Drowsiness Monitoring System using Visual Behaviour and Machine Learning.” 2018 IEEE Symp. on Computer Applications& Industrial Electronics (ISCAIE)339–344.

5. “Determination of optimal electroen-cephalography recording locations for detecting drowsy driving;Zhang;IET Intelligent Transport Systems,2018

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