TY - GEN
T1 - Emotion recognition using facial expressions in children using the NAO robot
AU - Lopez-Rincon, Alejandro
N1 - Funding Information:
This work is part of a project1 (#15198) that is included in the research program Technology for Oncology, which is financed by the Netherlands Organization for Scientific Research (NWO), the Dutch Cancer Society (KWF), the TKI Life Sciences & Health, Asolutions, Brocacef, Cancer Health Coach, and Focal Meditech. The research consortium consists of the Centrum Wiskunde & Informatica, Delft University of Technology, the Academic Medical Center, and the Princess Maxima Center.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/22
Y1 - 2019/3/22
N2 - The detection of human emotions from facial expressions is crucial for social interaction. Therefore, several systems of behavioral computing in robotics try to recognize human emotion from images and video, but most of them are trained to classify emotions in adults only. Using the standard of 6 basic emotions: Sadness, happiness, surprise, anger, disgust, and fear, we try to classify the facial expressions using the NAO robot in children. In this study, we make the comparison between the AFFDEX SDK, and a Convolution Neural Network (CNN) with Viola-Jones trained with the AffectNet dataset, and tuned with the NIMH-ChEF dataset using transfer learning to classify facial expressions in children. Then, we test our system comparing the CNN and the AFFDEX SDK for classification in the Child Affective Facial Expression (CAFE) dataset. Finally, we compare both systems using the NAO robot in a subset of the AM-FED and EmoReact datasets.
AB - The detection of human emotions from facial expressions is crucial for social interaction. Therefore, several systems of behavioral computing in robotics try to recognize human emotion from images and video, but most of them are trained to classify emotions in adults only. Using the standard of 6 basic emotions: Sadness, happiness, surprise, anger, disgust, and fear, we try to classify the facial expressions using the NAO robot in children. In this study, we make the comparison between the AFFDEX SDK, and a Convolution Neural Network (CNN) with Viola-Jones trained with the AffectNet dataset, and tuned with the NIMH-ChEF dataset using transfer learning to classify facial expressions in children. Then, we test our system comparing the CNN and the AFFDEX SDK for classification in the Child Affective Facial Expression (CAFE) dataset. Finally, we compare both systems using the NAO robot in a subset of the AM-FED and EmoReact datasets.
KW - CNN
KW - emotion recognition
KW - human robot interaction
UR - https://www.scopus.com/pages/publications/85064191934
U2 - 10.1109/CONIELECOMP.2019.8673111
DO - 10.1109/CONIELECOMP.2019.8673111
M3 - Conference contribution
AN - SCOPUS:85064191934
T3 - CONIELECOMP 2019 - 2019 International Conference on Electronics, Communications and Computers
SP - 146
EP - 153
BT - CONIELECOMP 2019 - 2019 International Conference on Electronics, Communications and Computers
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Electronics, Communications and Computers, CONIELECOMP 2019
Y2 - 27 February 2019 through 1 March 2019
ER -