Perception of drowsiness based on correlation with facial image features

Yugo Sato, Takuya Kato, Naoki Nozawa, Shigeo Morishima

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

Abstract

This paper presents a video-based method for detecting drowsiness. Generally, human beings can perceive their fatigue and drowsiness through looking at faces. The ability to perceive the fatigue and the drowsiness has been studied in many ways. The drowsiness detection method based on facial videos has been proposed [Nakamura et al. 2014]. In their method, a set of the facial features calculated with the Computer Vision techniques and the k-nearest neighbor algorithm are applied to classify drowsiness degree. However, the facial features that are ineffective against reproducing the perception of human beings with the machine learning method are not removed. This factor can decrease the detection accuracy.

Original languageEnglish
Title of host publicationProceedings of the ACM Symposium on Applied Perception, SAP 2016
PublisherAssociation for Computing Machinery, Inc
Pages139
Number of pages1
ISBN (Electronic)9781450343831
DOIs
Publication statusPublished - 2016 Jul 22
EventACM Symposium on Applied Perception, SAP 2016 - Anaheim, United States
Duration: 2016 Jul 222016 Jul 23

Publication series

NameProceedings of the ACM Symposium on Applied Perception, SAP 2016

Other

OtherACM Symposium on Applied Perception, SAP 2016
CountryUnited States
CityAnaheim
Period16/7/2216/7/23

Keywords

  • Correlation coefficient
  • Drowsiness detection
  • Face evaluation
  • Feature learning
  • K-nearest neighbor algorithm

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Software
  • Applied Mathematics
  • Theoretical Computer Science

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