Dominant and complementary emotion recognition from still images of faces

Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g.,...

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Detalhes bibliográficos
Autores: Guo, Jiazgu, Lei, Zhen, Wan, Jun, Avots, Egils, Hajarolasvadi, Noushin, Knyazev, Boris, Kuharenko, Artem, Silveira Jacques Junior, Julio Cezar, Baró, Xavier, Demirel, Hasan, Escalera, Sergio, Allik, Jüri, Anbarjafari, Gholamreza
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2018
País:España
Recursos:Universitat Oberta de Catalunya (UOC)
Repositório:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/93198
Acesso em linha:https://hdl.handle.net/10609/93198
Access Level:Acceso aberto
Palavra-chave:dominant and complementary emotion recognition
fine-grained face emotion dataset
compound emotions
conjunto de datos de emociones
emociones compuestas
reconocimiento de emociones dominantes y complementarias
conjunt de dades d'emocions
emocions compostes
reconeixement d'emocions dominants i complementàries
Biometry
Biometria
Biometría
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network_acronym_str ES
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repository_id_str
spelling Dominant and complementary emotion recognition from still images of facesGuo, JiazguLei, ZhenWan, JunAvots, EgilsHajarolasvadi, NoushinKnyazev, BorisKuharenko, ArtemSilveira Jacques Junior, Julio CezarBaró, XavierDemirel, HasanEscalera, SergioAllik, JüriAnbarjafari, Gholamrezadominant and complementary emotion recognitionfine-grained face emotion datasetcompound emotionsconjunto de datos de emocionesemociones compuestasreconocimiento de emociones dominantes y complementariasconjunt de dades d'emocionsemocions compostesreconeixement d'emocions dominants i complementàriesBiometryBiometriaBiometríaEmotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition.IEEE AccessChinese Academy of SciencesUniversity of TartuEastern Mediterranean UniversityNTechLabUniversitat de Barcelona (UB)Universitat Oberta de Catalunya (UOC)201920192018info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/10609/93198reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésIEEE Access, 2018, 6https://doi.org/10.1109/access.2018.2831927info:eu-repo/grantAgreement/PUT638//info:eu-repo/grantAgreement/IUT213//info:eu-repo/grantAgreement/TIN2015-66951-C2-2-R//info:eu-repo/grantAgreement/TIN2016-74946-P//info:eu-repo/grantAgreement/H2020-ICT-2015//info:eu-repo/grantAgreement/2016YFC0801002//info:eu-repo/grantAgreement/61502491//info:eu-repo/grantAgreement/61572501//info:eu-repo/grantAgreement/61572536//info:eu-repo/grantAgreement/61673052//info:eu-repo/grantAgreement/61473291//info:eu-repo/grantAgreement/61773392//info:eu-repo/grantAgreement/61403405//info:eu-repo/grantAgreement/116E097//(c) Author/s & (c) Journalinfo:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/931982026-05-28T12:42:01Z
dc.title.none.fl_str_mv Dominant and complementary emotion recognition from still images of faces
title Dominant and complementary emotion recognition from still images of faces
spellingShingle Dominant and complementary emotion recognition from still images of faces
Guo, Jiazgu
dominant and complementary emotion recognition
fine-grained face emotion dataset
compound emotions
conjunto de datos de emociones
emociones compuestas
reconocimiento de emociones dominantes y complementarias
conjunt de dades d'emocions
emocions compostes
reconeixement d'emocions dominants i complementàries
Biometry
Biometria
Biometría
title_short Dominant and complementary emotion recognition from still images of faces
title_full Dominant and complementary emotion recognition from still images of faces
title_fullStr Dominant and complementary emotion recognition from still images of faces
title_full_unstemmed Dominant and complementary emotion recognition from still images of faces
title_sort Dominant and complementary emotion recognition from still images of faces
dc.creator.none.fl_str_mv Guo, Jiazgu
Lei, Zhen
Wan, Jun
Avots, Egils
Hajarolasvadi, Noushin
Knyazev, Boris
Kuharenko, Artem
Silveira Jacques Junior, Julio Cezar
Baró, Xavier
Demirel, Hasan
Escalera, Sergio
Allik, Jüri
Anbarjafari, Gholamreza
author Guo, Jiazgu
author_facet Guo, Jiazgu
Lei, Zhen
Wan, Jun
Avots, Egils
Hajarolasvadi, Noushin
Knyazev, Boris
Kuharenko, Artem
Silveira Jacques Junior, Julio Cezar
Baró, Xavier
Demirel, Hasan
Escalera, Sergio
Allik, Jüri
Anbarjafari, Gholamreza
author_role author
author2 Lei, Zhen
Wan, Jun
Avots, Egils
Hajarolasvadi, Noushin
Knyazev, Boris
Kuharenko, Artem
Silveira Jacques Junior, Julio Cezar
Baró, Xavier
Demirel, Hasan
Escalera, Sergio
Allik, Jüri
Anbarjafari, Gholamreza
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Chinese Academy of Sciences
University of Tartu
Eastern Mediterranean University
NTechLab
Universitat de Barcelona (UB)
Universitat Oberta de Catalunya (UOC)
dc.subject.none.fl_str_mv dominant and complementary emotion recognition
fine-grained face emotion dataset
compound emotions
conjunto de datos de emociones
emociones compuestas
reconocimiento de emociones dominantes y complementarias
conjunt de dades d'emocions
emocions compostes
reconeixement d'emocions dominants i complementàries
Biometry
Biometria
Biometría
topic dominant and complementary emotion recognition
fine-grained face emotion dataset
compound emotions
conjunto de datos de emociones
emociones compuestas
reconocimiento de emociones dominantes y complementarias
conjunt de dades d'emocions
emocions compostes
reconeixement d'emocions dominants i complementàries
Biometry
Biometria
Biometría
description Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition.
publishDate 2018
dc.date.none.fl_str_mv 2018
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10609/93198
url https://hdl.handle.net/10609/93198
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Access, 2018, 6
https://doi.org/10.1109/access.2018.2831927
info:eu-repo/grantAgreement/PUT638//
info:eu-repo/grantAgreement/IUT213//
info:eu-repo/grantAgreement/TIN2015-66951-C2-2-R//
info:eu-repo/grantAgreement/TIN2016-74946-P//
info:eu-repo/grantAgreement/H2020-ICT-2015//
info:eu-repo/grantAgreement/2016YFC0801002//
info:eu-repo/grantAgreement/61502491//
info:eu-repo/grantAgreement/61572501//
info:eu-repo/grantAgreement/61572536//
info:eu-repo/grantAgreement/61673052//
info:eu-repo/grantAgreement/61473291//
info:eu-repo/grantAgreement/61773392//
info:eu-repo/grantAgreement/61403405//
info:eu-repo/grantAgreement/116E097//
dc.rights.none.fl_str_mv (c) Author/s & (c) Journal
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Author/s & (c) Journal
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE Access
publisher.none.fl_str_mv IEEE Access
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.198674

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