Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (18): 151-154.DOI: 10.3778/j.issn.1002-8331.1807-0105

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Multi-Task Middle-Level Feature Individualization Learning for Micro-Expression Recognition

LIU Zhen, WANG Sujing, LI Qing   

  1. 1.Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science Technology University, Beijing 100192, China
    2.Key Laboratory of Behavior Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
  • Online:2019-09-15 Published:2019-09-11

基于多任务中级特征个性化学习的微表情识别

刘振,王甦菁,李擎   

  1. 1.北京信息科技大学 高动态导航技术北京市重点实验室,北京 100192
    2.中国科学院 心理研究所 行为科学重点实验室,北京 100101

Abstract: Micro-expressions are fleeting facial expressions that expose a genuine emotions that a person tries to conceal. An existing multi-task middle-level feature learning is improved, and a multi-task middle-level feature individualization learning method for micro-expression recognition is proposed. The same kind of micro-expressions of the same person are removed for calculating its interclass nearest neighbors. the different kind of micro-expressions of the same person are retained and the [k] value is reduced for calculating its interclass nearest neighbors. A middle-level feature with more discriminative information is generated by individualization learning method. Experimental results on spontaneous micro-expression database CASME2 show that the proposed method has better recognition performance.

Key words: micro-expression recognition, individualization learning, middle-level feature

摘要: 微表情是一种短暂的面部表情,揭示了一个人试图隐藏的真实情感。对现有的一种多任务中级特征学习方法进行了改进,提出了一种多任务中级特征个性化学习方法用于微表情识别。对于每个低级特征,计算类内[k]最近邻时,去除同一人的同类微表情;计算类间[k]最近邻时,保留同一人的不同类表情,并减小[k]值。采用个性化学习方法,生成具有更多判别信息的中级特征。在微表情数据集CASME2上的实验表明,所提出的方法具有更好的识别性能。

关键词: 微表情识别, 个性化学习, 中级特征