Authors:
Taro Nakamura
;
Akinobu Maejima
and
Shigeo Morishima
Affiliation:
Waseda University, Japan
Keyword(s):
Drowsiness Level Estimation, Face Texture Analysis, Wrinkle Detection, Edge Intensity, K-NN, CG for CV, Investigating Drowsiness Feature.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
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.