Review of automatic microexpression recognition in the past decade

Liangfei Zhang*, Ognjen Arandjelović

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

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Abstract

Facial expressions provide important information concerning one’s emotional state. Unlike regular facial expressions, microexpressions are particular kinds of small quick facial movements, which generally last only 0.05 to 0.2 s. They reflect individuals’ subjective emotions and real psychological states more accurately than regular expressions which can be acted. However, the small range and short duration of facial movements when microexpressions happen make them challenging to recognize both by humans and machines alike. In the past decade, automatic microexpression recognition has attracted the attention of researchers in psychology, computer science, and security, amongst others. In addition, a number of specialized microexpression databases have been collected and made publicly available. The purpose of this article is to provide a comprehensive overview of the current state of the art automatic facial microexpression recognition work. To be specific, the features and learning methods used in automatic microexpression recognition, the existing microexpression data sets, the major outstanding challenges, and possible future development directions are all discussed.
Original languageEnglish
Pages (from-to)414-434
Number of pages21
JournalMachine Learning and Knowledge Extraction
Volume3
Issue number2
DOIs
Publication statusPublished - 2 May 2021

Keywords

  • Affective computing
  • Microexpression recognition
  • Emotion recognition
  • Microexpression database
  • Video feature extraction
  • Deep learning

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