Driver drowsiness estimation from facial expression features: Computer Vision Feature Investigation using a CG Model

Taro Nakamura, Akinobu Maejima, Shigeo Morishima

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

15 Citations (Scopus)

Abstract

We propose a method for estimating the degree of a driver's drowsiness on the basis of changes in facial expressions captured by an IR camera. Typically, drowsiness is accompanied by drooping eyelids. Therefore, most related studies have focused on tracking eyelid movement by monitoring facial feature points. However, the drowsiness feature emerges not only in eyelid movements but also in other facial expressions. To more precisely estimate drowsiness, we must select other effective features. In this study, we detected a new drowsiness feature by comparing a video image and CG model that are applied to the existing feature point information. In addition, we propose a more precise degree of drowsiness estimation method using wrinkle changes and calculating local edge intensity on faces, which expresses drowsiness more directly in the initial stage.

Original languageEnglish
Title of host publicationVISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications
PublisherSciTePress
Pages207-214
Number of pages8
ISBN (Print)9789897580048
DOIs
Publication statusPublished - 2014 Jan 1
Event9th International Conference on Computer Vision Theory and Applications, VISAPP 2014 - Lisbon, Portugal
Duration: 2014 Jan 52014 Jan 8

Publication series

NameVISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications
Volume2

Conference

Conference9th International Conference on Computer Vision Theory and Applications, VISAPP 2014
Country/TerritoryPortugal
CityLisbon
Period14/1/514/1/8

Keywords

  • CG for CV
  • Drowsiness Level Estimation
  • Edge Intensity
  • Face Texture Analysis
  • Investigating Drowsiness Feature
  • K-NN
  • Wrinkle Detection

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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