Driver Drowsiness Estimation from Facial Expression Features - Computer Vision Feature Investigation using a CG Model

Taro Nakamura, Akinobu Maejima, Shigeo Morishima

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.

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Paper Citation


in Harvard Style

Nakamura T., Maejima A. and Morishima S. (2014). Driver Drowsiness Estimation from Facial Expression Features - Computer Vision Feature Investigation using a CG Model . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 207-214. DOI: 10.5220/0004648902070214


in Bibtex Style

@conference{visapp14,
author={Taro Nakamura and Akinobu Maejima and Shigeo Morishima},
title={Driver Drowsiness Estimation from Facial Expression Features - Computer Vision Feature Investigation using a CG Model},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={207-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004648902070214},
isbn={978-989-758-004-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Driver Drowsiness Estimation from Facial Expression Features - Computer Vision Feature Investigation using a CG Model
SN - 978-989-758-004-8
AU - Nakamura T.
AU - Maejima A.
AU - Morishima S.
PY - 2014
SP - 207
EP - 214
DO - 10.5220/0004648902070214