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dc.contributor.authorZhang, Liangfei
dc.contributor.authorArandjelović, Ognjen
dc.date.accessioned2021-05-04T15:30:08Z
dc.date.available2021-05-04T15:30:08Z
dc.date.issued2021-05-02
dc.identifier.citationZhang , L & Arandjelović , O 2021 , ' Review of automatic microexpression recognition in the past decade ' , Machine Learning and Knowledge Extraction , vol. 3 , no. 2 , pp. 414-434 . https://doi.org/10.3390/make3020021en
dc.identifier.issn2504-4990
dc.identifier.otherPURE: 274063249
dc.identifier.otherPURE UUID: cb4eca70-7329-4344-a638-2e356c1186fe
dc.identifier.otherJisc: 8c4ba34675244311859324bb8baeb037
dc.identifier.urihttp://hdl.handle.net/10023/23112
dc.descriptionL.Z. is funded by the China Scholarship Council—University of St Andrews Scholarships (No.201908060250).en
dc.description.abstractFacial 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.
dc.format.extent21
dc.language.isoeng
dc.relation.ispartofMachine Learning and Knowledge Extractionen
dc.rightsCopyright © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).en
dc.subjectAffective computingen
dc.subjectMicroexpression recognitionen
dc.subjectEmotion recognitionen
dc.subjectMicroexpression databaseen
dc.subjectVideo feature extractionen
dc.subjectDeep learningen
dc.subjectBF Psychologyen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRC0321 Neuroscience. Biological psychiatry. Neuropsychiatryen
dc.subject.lccBFen
dc.subject.lccQA75en
dc.subject.lccRC0321en
dc.titleReview of automatic microexpression recognition in the past decadeen
dc.typeJournal itemen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.3390/make3020021
dc.description.statusPeer revieweden


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