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Systematic Review of Emotion Detection with Computer Vision and Deep Learning

2024-05-28Journal article Open access
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dc.contributor.authorPereira, Rafael
dc.contributor.authorMendes, Carla
dc.contributor.authorRibeiro, José
dc.contributor.authorRibeiro, Roberto
dc.contributor.authorMiragaia, Rolando
dc.contributor.authorRodrigues, Nuno
dc.contributor.authorCosta, Nuno
dc.contributor.authorPereira, António
dc.date.accessioned2024-08-02T11:44:11Z
dc.date.available2024-08-02T11:44:11Z
dc.date.issued2024-05-28
dc.date.updated2024-07-26T10:52:56Z
dc.descriptionFunding: This work was supported by national funds through the Portuguese Foundation for Science and Technology (FCT), I.P., under the project UIDB/04524/2020.pt_PT
dc.description.abstractEmotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human–computer interaction (HCI) in areas such as psychology, healthcare, and entertainment. In this paper, we conduct a systematic review of facial and pose emotion recognition using DL and computer vision, analyzing and evaluating 77 papers from different sources under Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) guidelines. Our review covers several topics, including the scope and purpose of the studies, the methods employed, and the used datasets. The scope of this work is to conduct a systematic review of facial and pose emotion recognition using DL methods and computer vision. The studies were categorized based on a proposed taxonomy that describes the type of expressions used for emotion detection, the testing environment, the currently relevant DL methods, and the datasets used. The taxonomy of methods in our review includes Convolutional Neural Network (CNN), Faster Region-based Convolutional Neural Network (R-CNN), Vision Transformer (ViT), and “Other NNs”, which are the most commonly used models in the analyzed studies, indicating their trendiness in the field. Hybrid and augmented models are not explicitly categorized within this taxonomy, but they are still important to the field. This review offers an understanding of state-of-the-art computer vision algorithms and datasets for emotion recognition through facial expressions and body poses, allowing researchers to understand its fundamental components and trends.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPereira, R.; Mendes, C.; Ribeiro, J.; Ribeiro, R.; Miragaia, R.; Rodrigues, N.; Costa, N.; Pereira, A. Systematic Review of Emotion Detection with Computer Vision and Deep Learning. Sensors 2024, 24(11), 3484. http://doi.org/10.3390/s24113484pt_PT
dc.identifier.doihttp://doi.org/10.3390/s24113484pt_PT
dc.identifier.eissn1424-8220
dc.identifier.slugcv-prod-4098960
dc.identifier.urihttp://hdl.handle.net/10400.8/9898
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationResearch Center in Informatics and Communications
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/24/11/3484pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectEmotion recognitionpt_PT
dc.subjectComputer visionpt_PT
dc.subjectDeep learningpt_PT
dc.subjectSystematic reviewpt_PT
dc.subjectEmotion detectionpt_PT
dc.titleSystematic Review of Emotion Detection with Computer Vision and Deep Learningpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Center in Informatics and Communications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04524%2F2020/PT
oaire.citation.issue11pt_PT
oaire.citation.startPage3484pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume24pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNamePereira
person.familyNameMendes
person.familyNameRibeiro
person.familyNameRibeiro
person.familyNameMiragaia
person.familyNameM. M. Rodrigues
person.familyNameCosta
person.familyNamePereira
person.givenNameRafael
person.givenNameCarla
person.givenNameJosé
person.givenNameRoberto
person.givenNameRolando
person.givenNameNuno
person.givenNameNuno
person.givenNameAntónio
person.identifier3595480
person.identifier.ciencia-idCA14-C0C8-F87D
person.identifier.ciencia-idF11F-7951-84BC
person.identifier.ciencia-id7F12-40BA-57CD
person.identifier.ciencia-idC712-E02E-0ED2
person.identifier.ciencia-idCB19-E7DA-5A7F
person.identifier.ciencia-idE215-4F0F-33EC
person.identifier.orcid0000-0001-8313-7253
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person.identifier.orcid0000-0001-9278-9296
person.identifier.orcid0000-0003-1547-4674
person.identifier.orcid0000-0003-4213-9302
person.identifier.orcid0000-0001-9536-1017
person.identifier.orcid0000-0002-2353-369X
person.identifier.orcid0000-0001-5062-1241
person.identifier.ridGLS-3615-2022
person.identifier.ridM-6163-2013
person.identifier.scopus-author-id26422369700
person.identifier.scopus-author-id7006052345
person.identifier.scopus-author-id56890641000
person.identifier.scopus-author-id7402230199
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.cv.cienciaidC712-E02E-0ED2 | Rolando Lúcio Germano Miragaia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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