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Emotion recognition using autoencoders and convolutional neural networks

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dc.title Emotion recognition using autoencoders and convolutional neural networks en
dc.contributor.author Beltrán-Prieto, Luis Antonio
dc.contributor.author Komínková Oplatková, Zuzana
dc.relation.ispartof Mendel
dc.identifier.issn 1803-3814 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2018
utb.relation.volume 24
utb.relation.issue 1
dc.citation.spage 113
dc.citation.epage 120
dc.type article
dc.language.iso en
dc.publisher Brno University of Technology
dc.identifier.doi 10.13164/mendel.2018.1.113
dc.relation.uri https://mendel-journal.org/index.php/mendel/article/view/31
dc.subject AutoEncoders en
dc.subject convolutional neural networks en
dc.subject deep learning en
dc.subject emotion recognition en
dc.description.abstract Emotions demonstrate people's reactions to certain stimuli. Facial expression analysis is often used to identify the emotion expressed. Machine learning algorithms combined with artificial intelligence techniques have been developed in order to detect expressions found in multimedia elements, including videos and pictures. Advanced methods to achieve this include the usage of Deep Learning algorithms. The aim of this paper is to analyze the performance of a Convolutional Neural Network which uses AutoEncoder Units for emotion-recognition in human faces. The combination of two Deep Learning techniques boosts the performance of the classification system. 8000 facial expressions from the Radboud Faces Database were used during this research for both training and testing. The outcome showed that five of the eight analyzed emotions presented higher accuracy rates, higher than 90%. © 2018, Brno University of Technology. All rights reserved. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1009090
utb.identifier.obdid 43879002
utb.identifier.scopus 2-s2.0-85072024940
utb.source j-scopus
dc.date.accessioned 2019-09-19T07:56:16Z
dc.date.available 2019-09-19T07:56:16Z
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.access openAccess
utb.ou CEBIA-Tech
utb.contributor.internalauthor Beltrán-Prieto, Luis Antonio
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.fulltext.affiliation Luis Antonio Beltrán Prieto, Zuzana Komínková Oplatková Tomas Bata University in Zlín Faculty of Applied Informatics Nám. T. G. Masaryka 5555, 76001 Zlín Czech Republic beltran_prieto@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.sponsorship This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014), further by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2018/003. This work is also based upon support by COST (European Cooperation in Science & Technology) under Action CA15140, Improving Applicability of Nature- Inspired Optimisation by Joining Theory and Practice (ImAppNIO), and Action IC1406, High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). The work was further supported by resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz). L.A.B.P author also thanks the
utb.scopus.affiliation Tomas Bata University in Zlín, Faculty of Applied Informatics, Nám. T. G. Masaryka 5555, Zlín, 76001, Czech Republic
utb.fulltext.projects LO1303
utb.fulltext.projects MSMT-7778/2014
utb.fulltext.projects CZ.1.05/2.1.00/03.0089
utb.fulltext.projects IGA/CebiaTech/2018/003
utb.fulltext.projects CA15140
utb.fulltext.projects IC1406
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
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