Automated facial expression recognition using exemplar hybrid deep feature generation technique

Baygin, M; Tuncer, I; Dogan, S; Barua, PD; Tuncer, T; Cheong, KH; Acharya, UR

Automated facial expression recognition using exemplar hybrid deep feature generation technique

Baygin, M Tuncer, I Dogan, S Barua, PD Tuncer, T Cheong, KH Acharya, UR

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Soft Computing, 2023, 27, (13), pp. 8721-8737
Issue Date:: 2023-07-01

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Field	Value	Language
dc.contributor.author	Baygin, M
dc.contributor.author	Tuncer, I
dc.contributor.author	Dogan, S
dc.contributor.author	Barua, PD
dc.contributor.author	Tuncer, T
dc.contributor.author	Cheong, KH
dc.contributor.author	Acharya, UR
dc.date.accessioned	2024-04-05T00:37:54Z
dc.date.available	2024-04-05T00:37:54Z
dc.date.issued	2023-07-01
dc.identifier.citation	Soft Computing, 2023, 27, (13), pp. 8721-8737
dc.identifier.issn	1432-7643
dc.identifier.issn	1433-7479
dc.identifier.uri	http://hdl.handle.net/10453/177485
dc.description.abstract	The perception and recognition of emotional expressions provide essential information about individuals’ social behavior. Therefore, decoding emotional expressions is very important. Facial expression recognition (FER) is one of the most frequently studied topics. An accurate FER model has four prime phases. (i) Facial areas are segmented from the face images. (ii) An exemplar deep feature-based model is proposed. Two pretrained deep models (AlexNet and MobileNetV2) are utilized as feature generators. By merging both pretrained networks, a feature generation function is presented. (iii) The most valuable 1000 features are selected by neighborhood component analysis (NCA). (iv) These 1000 features are selected on a support vector machine (SVM). We have developed our model using five FER corpora: TFEID, JAFFE, KDEF, CK+, and Oulu-CASIA. Our developed model is able to yield an accuracy of 97.01, 98.59, 96.54, 100, and 100%, using TFEID, JAFFE, KDEF, CK+, and Oulu-CASIA, respectively. The results obtained in this study showed that the proposed exemplar deep feature extraction approach has obtained high success rates in the automatic FER method using various databases.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Soft Computing
dc.relation.isbasedon	10.1007/s00500-023-08230-9
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.title	Automated facial expression recognition using exemplar hybrid deep feature generation technique
dc.type	Journal Article
utslib.citation.volume	27
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
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 Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-05T00:37:53Z
pubs.issue	13
pubs.publication-status	Published
pubs.volume	27
utslib.citation.issue	13

Abstract:

The perception and recognition of emotional expressions provide essential information about individuals’ social behavior. Therefore, decoding emotional expressions is very important. Facial expression recognition (FER) is one of the most frequently studied topics. An accurate FER model has four prime phases. (i) Facial areas are segmented from the face images. (ii) An exemplar deep feature-based model is proposed. Two pretrained deep models (AlexNet and MobileNetV2) are utilized as feature generators. By merging both pretrained networks, a feature generation function is presented. (iii) The most valuable 1000 features are selected by neighborhood component analysis (NCA). (iv) These 1000 features are selected on a support vector machine (SVM). We have developed our model using five FER corpora: TFEID, JAFFE, KDEF, CK+, and Oulu-CASIA. Our developed model is able to yield an accuracy of 97.01, 98.59, 96.54, 100, and 100%, using TFEID, JAFFE, KDEF, CK+, and Oulu-CASIA, respectively. The results obtained in this study showed that the proposed exemplar deep feature extraction approach has obtained high success rates in the automatic FER method using various databases.

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http://hdl.handle.net/10453/177485