Kernelized Fuzzy Rough Sets Based Yawn Detection for Driver Fatigue Monitoring

Authors:
Yong Du;Qinghua Hu;Degang Chen;Peijun Ma
Affiliations:
Harbin Institute of Technology, Harbin 150001, P.R.China. hitduyong@gmail.com;(Correspd.) Post Box 458, Harbin Institute of Technology, Harbin 150001, P.R.China. huqinghua@hit.edu.cn;North China Electric Power University, Beijing, P.R.China. chengdegang@263.net;Harbin Institute of Technology, Harbin 150001, P.R.China. ma@hit.edu.cn
Venue:
Fundamenta Informaticae - Knowledge Technology
Year:
2011

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Abstract

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.