Eye fatigue algorithm for driver drowsiness detection system

Teik Jin Lim*, Hung Yang Leong, Jia Yew Pang, Mohd Rizon Mohamed Juhari

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper proposed an algorithm that used the combination of the Viola-Jones technique, Circular Hough Transform (CHT), Histogram Equalization, Canny Edge detection and percentage of eyelid closure (PERCLOS) technique to accurately detect the eyes condition of the driver. This proposed algorithm achieved a 92.5% accuracy of eyes open images detection and 86.65% accuracy of eyes close images detection with 50 samples each. For the real-time video processing, it achieved 90% accuracy during daytime and 86% accuracy during night-time for the eyes drowsiness detection.

Original languageEnglish
Title of host publicationImage Processing and Capsule Networks. ICIPCN 2020
EditorsJoy Iong-Zong Chen, João Manuel R.S. Tavares, Subarna Shakya, Abdullah M. Iliyasu
Number of pages15
ISBN (Electronic)9783030518592
ISBN (Print)9783030518585
Publication statusPublished - 2021
EventInternational Conference on Image Processing and Capsule Networks 2020 - Bangkok, Thailand
Duration: 6 May 20207 May 2020

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceInternational Conference on Image Processing and Capsule Networks 2020
Abbreviated titleICIPCN 2020


  • Circular Hough Transform
  • Drowsiness detection
  • Eyes detection
  • Eyes state analysis
  • Image processing
  • Viola-Jones

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)


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