TY - GEN
T1 - Driver drowsiness estimation from facial expression features
T2 - 9th International Conference on Computer Vision Theory and Applications, VISAPP 2014
AU - Nakamura, Taro
AU - Maejima, Akinobu
AU - Morishima, Shigeo
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - CG for CV
KW - Drowsiness Level Estimation
KW - Edge Intensity
KW - Face Texture Analysis
KW - Investigating Drowsiness Feature
KW - K-NN
KW - Wrinkle Detection
UR - http://www.scopus.com/inward/record.url?scp=84906895779&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84906895779&partnerID=8YFLogxK
U2 - 10.5220/0004648902070214
DO - 10.5220/0004648902070214
M3 - Conference contribution
AN - SCOPUS:84906895779
SN - 9789897580048
T3 - VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications
SP - 207
EP - 214
BT - VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications
PB - SciTePress
Y2 - 5 January 2014 through 8 January 2014
ER -