计算机应用 ›› 2011, Vol. 31 ›› Issue (08): 2119-2122.

• 人工智能 • 上一篇    下一篇

基于Gabor小波变换的人脸疲劳模式识别

成奋华1,杨海燕2   

  1. 1. 湖南科技职业学院 电子信息工程与技术系,长沙410004
    2. 中南大学 信息科学与工程学院,长沙410083
  • 收稿日期:2011-01-31 修回日期:2011-03-14 发布日期:2011-08-01 出版日期:2011-08-01
  • 通讯作者: 成奋华
  • 作者简介:成奋华(1969-),男,湖南长沙人,副教授,硕士,主要研究方向:计算机网络、软件工程;杨海燕(1980-),女,湖南益阳人,博士,讲师,主要研究方向:计算机网络、软件工程。

Fatigue pattern recognition of human face based on Gabor wavelet transform

Fen-hua CHENG1,Hai-yan YANG2   

  1. 1. Department of Electronic Information Engineering and Technology, Hunan Vocational College of Science and Technology, Changsha Hunan 410004, China
    2. School of Information Science and Engineering, Central South University, Changsha Hunan 410083, China
  • Received:2011-01-31 Revised:2011-03-14 Online:2011-08-01 Published:2011-08-01
  • Contact: Fen-hua CHENG

摘要: 疲劳是造成交通事故的主因之一,提出了一种基于Gabor小波变换的疲劳监控新方法。首先,在训练阶段采用频繁模式挖掘算法对疲劳脸部图像序列集进行疲劳模式挖掘;然后,在疲劳识别阶段,将待检测的脸部图像序列基于Gabor小波变换表示为融合特征序列;最后,采用分类算法进行人脸序列的疲劳检测。对自行收集的一天内500幅疲劳图像的仿真结果表明,所提方法正确检测率达到92.8%,错误检测率达到0.02%,优于比较算法。

关键词: 疲劳模式, Gabor小波变换, 频繁模式, 图像序列

Abstract: Fatigue is one of the main factors that cause traffic accidents. A new method for monitoring fatigue state based on Gabor wavelet transform was proposed. In this method, the frequent patterns mining algorithm was designed to mine the fatigue patterns of fatigue facial image sequences during the training phase first. And then, during the fatigue recognition phase, the face image sequence to be detected was represented by fused feature sequence through Gabor wavelet transform. Afterwards, the classification algorithm was used for fatigue detection of the human face sequence. The simulation results on 500 fatigue images sampled by the authors show that the proposed algorithm achieves 92.8% in right detection rate and 0.02% in error detection rate, and outperforms than some similar method.

Key words: fatigue pattern, Gabor wavelet transform, frequent pattern, image sequence

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