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Group Emotion Recognition Strategies for Entertainment Robots

Authors :
Jia-Yeu Lin
Thomas Pellegrini
Atsuo Takanishi
Estelle I. S. Randria
Sarah Cosentino
Salvatore Sessa
Department of Science and Engineering [University of Waseda]
Waseda University
Équipe Structuration, Analyse et MOdélisation de documents Vidéo et Audio (IRIT-SAMoVA)
Institut de recherche en informatique de Toulouse (IRIT)
Université Toulouse 1 Capitole (UT1)
Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3)
Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP)
Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1)
Université Fédérale Toulouse Midi-Pyrénées
Humanoid Robotics Institute (HRI)
Waseda University 2016 Grant-in-Aid for particular research subjects [2016S-081]
JSPS Grant-in-Aid for Young Scientists (Wakate B) [17K18178]
ST Microelectronics
ST Microsoft
Source :
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE/RSJ International Conference on Intelligent RObots and Systems-IROS 2018, IEEE/RSJ International Conference on Intelligent RObots and Systems-IROS 2018, Oct 2018, Madrid, Spain. pp.813-818, ⟨10.1109/IROS.2018.8593503⟩, IROS
Publication Year :
2018
Publisher :
HAL CCSD, 2018.

Abstract

International audience; In this paper, a system to determine the emotion of a group of people via facial expression analysis is proposed for the Waseda Entertainment Robots. General models and standard methods for emotion definition and recognition are briefly described, as well as strategies for computing the group global emotion, knowing the individual emotions of group members. This work is based on Ekman's extended “Big Six” emotional model, popular in Computer Science and Affective Computing. Emotion recognition via facial expression analysis is performed with a cloud-computing based solution, using Microsoft Azure Cognitive services. First, the performances of both the Face API to detect faces, and Emotion API, to compute emotion via face expression analysis, are tested. After that, a solution to compute the emotion of a group of people has been implemented and its performances compared to human perceptions. This work presents concepts and strategies which can be generalized for applications within the scope of assistive robotics and, more broadly, affective computing, wherever it will be necessary to determine the emotion of a group of people

Details

Language :
English
Database :
OpenAIRE
Journal :
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE/RSJ International Conference on Intelligent RObots and Systems-IROS 2018, IEEE/RSJ International Conference on Intelligent RObots and Systems-IROS 2018, Oct 2018, Madrid, Spain. pp.813-818, ⟨10.1109/IROS.2018.8593503⟩, IROS
Accession number :
edsair.doi.dedup.....9cd7e493e9b09caad5e608d522461da6
Full Text :
https://doi.org/10.1109/IROS.2018.8593503⟩