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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/4B555N2
Repositorysid.inpe.br/sibgrapi/2024/04.11.21.54
Last Update2024:04.11.21.54.39 (UTC) murilo.preto@aluno.ufabc.edu.br
Metadata Repositorysid.inpe.br/sibgrapi/2024/04.11.21.54.39
Metadata Last Update2024:04.11.21.54.39 (UTC) murilo.preto@aluno.ufabc.edu.br
Citation KeyPretoFerrKura:2023:CoStAu
TitleComparison Study of Automated Facial Expression Recognition Models
Short TitleComparison Study of Automated Facial Expression Recognition Models
FormatOn-line
Year2023
Access Date2024, Apr. 29
Number of Files1
Size854 KiB
2. Context
Author1 Preto, Murilo de Souza
2 Ferreira, Fernando Teubl
3 Kurashima, Celso Setsuo
Affiliation1 Universidade Federal do ABC
2 Universidade Federal do ABC
3 Universidade Federal do ABC
EditorClua, Esteban Walter Gonzalez
Körting, Thales Sehn
Paulovich, Fernando Vieira
Feris, Rogerio
e-Mail Addressmurilo.preto@aluno.ufabc.edu.br
Conference NameConference on Graphics, Patterns and Images, 36 (SIBGRAPI)
Conference LocationRio Grande, RS
DateNov. 06-09, 2023
Book TitleProceedings
Tertiary TypeUndergraduate Work
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsfacial expression recognition
image processing
comparative evaluation
AbstractFacial expressions play a crucial role in human non-verbal communication, and in the psychology field there is a strong consensus on the existence of five key emotions: anger, fear, disgust, sadness, and happiness. This paper aims to evaluate multiple facial expression recognition detection models, assessing their performance across different machines and databases. By identifying the strengths and weaknesses of each option, the study seeks to comparatively determine the most suitable model for specific tasks or scenarios. For each computer, all databases were processed through the usage of the detection models, while measuring the required runtime for the facial expression detection. The detection models: Residual Masking Network and Deepface, were tested through the databases Extended Cohn-Kanade and AffectNet. The assessed data point towards an average higher accuracy for the model Residual Masking Network, but faster runtime for Deepface. Thereby, Deepface may be preferentially employed in scenarios where time constraints are a primary concern, there is limited processing capability available, or an emphasis on recognizing either happiness or neutral expressions, while Residual Masking Network might be favored in striving for a higher detection accuracy.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/4B555N2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/4B555N2
Languageen
Target FilePretoSIBGRAPI.pdf
User Groupmurilo.preto@aluno.ufabc.edu.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session sponsor subject tertiarymark type url versiontype volume
7. Description control
e-Mail (login)murilo.preto@aluno.ufabc.edu.br
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