TY - GEN
T1 - Group emotion recognition strategies for entertainment robots
AU - Cosentino, Sarah
AU - Randria, Estelle I.S.
AU - Lin, Jia Yeu
AU - Pellegrini, Thomas
AU - Sessa, Salvatore
AU - Takanishi, Atsuo
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/27
Y1 - 2018/12/27
N2 - 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.
AB - 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.
KW - assistive robotics
KW - emotion recognition
KW - entertainment robot
KW - humanoid robot
UR - http://www.scopus.com/inward/record.url?scp=85062984749&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062984749&partnerID=8YFLogxK
U2 - 10.1109/IROS.2018.8593503
DO - 10.1109/IROS.2018.8593503
M3 - Conference contribution
AN - SCOPUS:85062984749
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 813
EP - 818
BT - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
Y2 - 1 October 2018 through 5 October 2018
ER -