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作者:

Fan, Xiao (Fan, Xiao.) | Yin, Bao-Cai (Yin, Bao-Cai.) (学者:尹宝才) | Sun, Yan-Feng (Sun, Yan-Feng.) (学者:孙艳丰)

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摘要:

To improve driving safety, the authors propose an approach to locate a driver's mouth by a web camera and extract texture features from mouth corners for monitoring drivers' yawning. Firstly, it detects drivers' left and right mouth corners by gray projection based on the result of driver face detection, and then it extracts texture features of drivers' mouth corners by Gabor wavelets. Finally, LDA is used to classify Gabor features for yawning detection. The proposed approach is tested on 3000 images from thirty subjects with variations in illuminations, poses, and facial accessories (glasses). Yawning is also detected by the ratio of mouth height to width as a baseline. Experiment results show that the proposed approach is suitable for real time yawning detection, Gabor features are more powerful than geometric features for yawning representation, and an average recognition rate of 91.97% is achieved which is much better than the baseline.

关键词:

Computer vision Discriminant analysis Edge detection Face recognition Fatigue of materials Feature extraction Textures

作者机构:

  • [ 1 ] [Fan, Xiao]Beijing Key Laboratory of Multimedia and Intelligent Software, College of Computer Science and Technology, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Yin, Bao-Cai]Beijing Key Laboratory of Multimedia and Intelligent Software, College of Computer Science and Technology, Beijing University of Technology, Beijing 100124, China
  • [ 3 ] [Sun, Yan-Feng]Beijing Key Laboratory of Multimedia and Intelligent Software, College of Computer Science and Technology, Beijing University of Technology, Beijing 100124, China

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来源 :

Journal of Beijing University of Technology

ISSN: 0254-0037

年份: 2009

期: 3

卷: 35

页码: 409-413,432

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