Driver status recognition by neighborhood covering rules

Authors:
Yong Du;Qinghua Hu;Peijun Ma;Xiaohong Su
Affiliations:
Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China
Venue:
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Year:
2011

Citing 11
Cited 1

An Algorithm for Real-Time Stereo Vision Implementation of Head Pose and Gaze Direction Measurement

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A novel method for detecting lips, eyes and faces in real time

Real-Time Imaging - Special issue on spectral imaging
Margin based feature selection - theory and algorithms

ICML '04 Proceedings of the twenty-first international conference on Machine learning
A visual approach for driver inattention detection

Pattern Recognition
A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets

Information Sciences: an International Journal
On Three Types of Covering-Based Rough Sets

IEEE Transactions on Knowledge and Data Engineering
A systematic study on attribute reduction with rough sets based on general binary relations

Information Sciences: an International Journal
On Minimal Rule Sets for Almost All Binary Information Systems

Fundamenta Informaticae - Half a Century of Inspirational Research: Honoring the Scientific Influence of Antoni Mazurkiewicz
Neighborhood rough set based heterogeneous feature subset selection

Information Sciences: an International Journal
Reduction about approximation spaces of covering generalized rough sets

International Journal of Approximate Reasoning
Rule learning for classification based on neighborhood covering reduction

Information Sciences: an International Journal

Hybrid computer vision system for drivers' eye recognition and fatigue monitoring

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Driver fatigue recognition based on computer vision is considered as a challenging issue. Though human face carries most information related to human status, the information is redundant and overlapped. In this work, we concentrate on several fatigue indicating areas and three types of features are extracted from them. Then a neighborhood rough set technique is introduced to evaluate quality of candidate features and select the effective subset. A rule learning classifier based on neighborhood covering reduction is employed for the classification task. Compared with classic classifiers, the designed recognition system performs well. The experiments are presented to show the effectiveness of the proposed technique.