Kernelized Fuzzy Rough Sets Based Yawn Detection for Driver Fatigue Monitoring

被引：11

作者：

Du, Yong ^{[1
]}

Chen, Degang ^{[2
]}

Hu, Qinghua ^{[1
]}

Ma, Peijun ^{[1
]}

机构：

[1] Harbin Inst Technol, Harbin 150001, Peoples R China

[2] N China Elect Power Univ, Beijing, Peoples R China

来源：

FUNDAMENTA INFORMATICAE | 2011年 / 111卷 / 01期

关键词：

fatigue detection; image recognition; fuzzy rough sets; feature selection; classification; GAZE; EYE;

D O I：

10.3233/FI-2011-554

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Driver fatigue detection based on computer vision is considered as one of the most hopeful applications of image recognition technology. The key issue is to extract and select useful features from the driver images. In this work, we use the properties of image sequences to describe states of drivers. In addition, we introduce a kernelized fuzzy rough sets based technique to evaluate quality of candidate features and select the useful subset. Fuzzy rough sets are widely discussed in dealing with uncertainty in data analysis. We construct an algorithm for feature evaluation and selection based on fuzzy rough set model. Two classification algorithms are introduced to validate the selected features. The experimental results show the effectiveness of the proposed techniques.

引用

页码：65 / 79

页数：15

共 24 条

[21] Learning fuzzy rules from fuzzy samples based on rough set technique [J].