Manifold feature integration for micro-expression recognition

Takalkar, MA; Xu, M; Chaczko, Z

Manifold feature integration for micro-expression recognition

Takalkar, MA Xu, M

Chaczko, Z

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Multimedia Systems, 2020
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Takalkar, MA
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849
dc.contributor.author	Chaczko, Z https://orcid.org/0000-0002-2816-7510
dc.date.accessioned	2020-07-07T11:49:18Z
dc.date.available	2020-07-07T11:49:18Z
dc.date.issued	2020-01-01
dc.identifier.citation	Multimedia Systems, 2020
dc.identifier.issn	0942-4962
dc.identifier.issn	1432-1882
dc.identifier.uri	http://hdl.handle.net/10453/141738
dc.description.abstract	© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Recognition of micro-expressions depends on the key features provided in the form of the temporal information. It needs considerable effort, however, to manually design useful characteristics. Subtle or micro-facial expressions are much difficult than regular facial expressions rich in emotional expressions in a true environment to be identified. An easy solution is discussed in this paper to recognise facial micro-expressions that utilizes an algorithm mix for facial identification, feature extraction and classification. The technique proposed is a framework which incorporates handcrafted features and deep features. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) is the handcraft feature which combines spatial and time analysis to encapsulate regional facet movements. The deep feature model is a micro-expression fine-tuned model based on Convolutional Neural Network (CNN). Two classifiers, i.e. SVM and Softmax are trained with combined feature vectors produced by LBP-TOP and CNN functionalities. All seven widely-used micro-expression databases are evaluated in an experiment. Our research can be claimed as the first extensive experimental study on a big amount of the datasets to train and test the suggested model. The findings in the document show that the method proposed, although simple and straightforward, achieves a substantial increase in precision relative to other commonly recognized micro-expression techniques, which are trained and tested with just a few datasets.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Multimedia Systems
dc.relation.isbasedon	10.1007/s00530-020-00663-8
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems, 0801 Artificial Intelligence and Image Processing
dc.subject.classification	Software Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Manifold feature integration for micro-expression recognition
dc.type	Journal Article
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
utslib.for	0801 Artificial Intelligence and 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/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2020-07-07T11:49:13Z
pubs.publication-status	Published

Abstract:

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Recognition of micro-expressions depends on the key features provided in the form of the temporal information. It needs considerable effort, however, to manually design useful characteristics. Subtle or micro-facial expressions are much difficult than regular facial expressions rich in emotional expressions in a true environment to be identified. An easy solution is discussed in this paper to recognise facial micro-expressions that utilizes an algorithm mix for facial identification, feature extraction and classification. The technique proposed is a framework which incorporates handcrafted features and deep features. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) is the handcraft feature which combines spatial and time analysis to encapsulate regional facet movements. The deep feature model is a micro-expression fine-tuned model based on Convolutional Neural Network (CNN). Two classifiers, i.e. SVM and Softmax are trained with combined feature vectors produced by LBP-TOP and CNN functionalities. All seven widely-used micro-expression databases are evaluated in an experiment. Our research can be claimed as the first extensive experimental study on a big amount of the datasets to train and test the suggested model. The findings in the document show that the method proposed, although simple and straightforward, achieves a substantial increase in precision relative to other commonly recognized micro-expression techniques, which are trained and tested with just a few datasets.

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http://hdl.handle.net/10453/141738