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基于差分能量图和CGBP的微表情识别

于明 王英资 于洋 刘依

于明, 王英资, 于洋, 刘依. 基于差分能量图和CGBP的微表情识别[J]. 西南交通大学学报, 2016, 29(6): 1222-1229. doi: 10.3969/j.issn.0258-2724.2016.06.024
引用本文: 于明, 王英资, 于洋, 刘依. 基于差分能量图和CGBP的微表情识别[J]. 西南交通大学学报, 2016, 29(6): 1222-1229. doi: 10.3969/j.issn.0258-2724.2016.06.024
YU Ming, WANG Yingzi, YU Yang, LIU Yi. Micro-expression Recognition Based on Differential Energy Maps and CGBP[J]. Journal of Southwest Jiaotong University, 2016, 29(6): 1222-1229. doi: 10.3969/j.issn.0258-2724.2016.06.024
Citation: YU Ming, WANG Yingzi, YU Yang, LIU Yi. Micro-expression Recognition Based on Differential Energy Maps and CGBP[J]. Journal of Southwest Jiaotong University, 2016, 29(6): 1222-1229. doi: 10.3969/j.issn.0258-2724.2016.06.024

基于差分能量图和CGBP的微表情识别

doi: 10.3969/j.issn.0258-2724.2016.06.024
基金项目: 

国家自然科学基金资助项目(60302018)

天津市科技计划资助项目(14RCGFGX00846)

河北省自然科学基金资助项目(F2015202239)

详细信息
    作者简介:

    于明(1964-),男,教授,博士,研究方向为图像处理与模式识别、智能媒体接口及生物信息识别,E-mail:yuming@hebut.edu.cn

Micro-expression Recognition Based on Differential Energy Maps and CGBP

  • 摘要: 微表情是一种不能自主控制和伪装的面部表情,其与诚信度的关系密切,具有持续时间短且难以识别的特征.为提高计算机自动识别微表情的准确性,提出一种基于差分能量图和中心化Gabor二值模式(centralized Gabor binary patterns,CGBP)的微表情识别方法.该方法首先利用差分法计算微表情序列的能量得到差分能量图,获得人脸面部肌肉相位的变化;其次将Gabor与中心二值模式CBP相结合,得到CGBP算子对能量图进行微表情的特征提取;最后利用ELM分类器进行微表情分类识别.在CASME微表情库上的实验结果表明,该方法比LBP-TOP、DTSA3、Gabor、VLBP、CBP-TOP算法更能有效地获得微表情序列的时空纹理特征,平均识别率为86.54%.

     

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出版历程
  • 收稿日期:  2015-05-20
  • 刊出日期:  2016-12-25

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