Improving Micro-expression Recognition Accuracy Using Twofold Feature Extraction

Takalkar, MA; Zhang, H; Xu, M

Improving Micro-expression Recognition Accuracy Using Twofold Feature Extraction

Takalkar, MA Zhang, H

Xu, M

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: MultiMedia Modeling (LNCS), 2019, 11295, pp. 652-664
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Takalkar, MA
dc.contributor.author	Zhang, H https://orcid.org/0000-0002-0021-3634
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849
dc.date	2019-01-08
dc.date.accessioned	2020-07-07T11:48:23Z
dc.date.available	2020-07-07T11:48:23Z
dc.date.issued	2019-01-01
dc.identifier.citation	MultiMedia Modeling (LNCS), 2019, 11295, pp. 652-664
dc.identifier.isbn	9783030057091
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/141737
dc.description.abstract	© 2019, Springer Nature Switzerland AG. Micro-expressions are generated involuntarily on a person’s face and are usually a manifestation of repressed feelings of the person. Micro-expressions are characterised by short duration, involuntariness and low intensity. Because of these characteristics, micro-expressions are difficult to perceive and interpret correctly, and they are profoundly challenging to identify and categorise automatically. Previous work for micro-expression recognition has used hand-crafted features like LBP-TOP, Gabor filter, HOG and optical flow. Recent work also has demonstrated the possible use of deep learning for micro-expression recognition. This paper is the first work to explore the use of hand-craft feature descriptor and deep feature descriptor for micro-expression recognition task. The aim is to use the hand-craft and deep learning feature descriptor to extract features and integrate them together to construct a large feature vector to describe a video. Through experiments on CASME, CASME II and CASME+2 databases, we demonstrate our proposed method can achieve promising results for micro-expression recognition accuracy with larger training samples.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	MultiMedia Modeling (LNCS)
dc.relation.ispartof	International Conference on Multimedia Modeling
dc.relation.isbasedon	10.1007/978-3-030-05710-7_54
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Improving Micro-expression Recognition Accuracy Using Twofold Feature Extraction
dc.type	Conference Proceeding
utslib.citation.volume	11295
utslib.location.activity	Thessaloniki, Greece
utslib.for	080106 Image Processing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-07-07T11:48:19Z
pubs.finish-date	2019-01-11
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2019-01-08
pubs.volume	11295
dc.location	Switzerland

Abstract:

© 2019, Springer Nature Switzerland AG. Micro-expressions are generated involuntarily on a person’s face and are usually a manifestation of repressed feelings of the person. Micro-expressions are characterised by short duration, involuntariness and low intensity. Because of these characteristics, micro-expressions are difficult to perceive and interpret correctly, and they are profoundly challenging to identify and categorise automatically. Previous work for micro-expression recognition has used hand-crafted features like LBP-TOP, Gabor filter, HOG and optical flow. Recent work also has demonstrated the possible use of deep learning for micro-expression recognition. This paper is the first work to explore the use of hand-craft feature descriptor and deep feature descriptor for micro-expression recognition task. The aim is to use the hand-craft and deep learning feature descriptor to extract features and integrate them together to construct a large feature vector to describe a video. Through experiments on CASME, CASME II and CASME+2 databases, we demonstrate our proposed method can achieve promising results for micro-expression recognition accuracy with larger training samples.

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http://hdl.handle.net/10453/141737