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http://hdl.handle.net/10609/93198
Title: Dominant and complementary emotion recognition from still images of faces
Author: Guo, Jiazgu
Lei, Zhen
Wan, Jun
Avots, Egils
Hajarolasvadi, Noushin
Knyazev, Boris
Kuharenko, Artem
Silveira Jacques Junior, Julio Cezar
Baró Solé, Xavier  
Demirel, Hasan
Escalera Guerrero, Sergio
Allik, Jüri
Anbarjafari, Gholamreza
Others: Chinese Academy of Sciences
University of Tartu
Eastern Mediterranean University
NTechLab
Universitat de Barcelona
Universitat Oberta de Catalunya (UOC)
Keywords: dominant and complementary emotion recognition
fine-grained face emotion dataset
compound emotions
Issue Date: 1-Jan-2018
Publisher: IEEE Access
Citation: Guo, J., Lei, Z., Wan, J., Avots, E., Hajarolasvadi, N., Knyazev, B., Kuharenko, A., Silveira Jacques Junior, J.C., Baró Solé, X., Demirel, H., Escalera Guerrero, S., Allik, J. & Anbarjafari, G. (2018). Dominant and Complementary Emotion Recognition from Still Images of Faces. IEEE Access, 6(), 26391-26403. doi: 10.1109/ACCESS.2018.2831927
Project identifier: 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
Also see: https://doi.org/10.1109/access.2018.2831927
Abstract: 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.
Language: English
URI: http://hdl.handle.net/10609/93198
ISSN: 2169-3536MIAR
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