Hybrid Models for Facial Emotion Recognition in Children
Rafael Zimmer Marcos Sobral Helio Azevedo
This paper focuses on the use of emotion recognition techniques to assist
psychologists in performing children's therapy through remotely robot operated
sessions. In the field of psychology, the use of agent-mediated therapy is
growing increasingly given recent advances in robotics and computer science.
Specifically, the use of Embodied Conversational Agents (ECA) as an
intermediary tool can help professionals connect with children who face social
challenges such as Attention Deficit Hyperactivity Disorder (ADHD), Autism
Spectrum Disorder (ASD) or even who are physically unavailable due to being in
regions of armed conflict, natural disasters, or other circumstances. In this
context, emotion recognition represents an important feedback for the
psychotherapist. In this article, we initially present the result of a
bibliographical research associated with emotion recognition in children. This
research revealed an initial overview on algorithms and datasets widely used by
the community. Then, based on the analysis carried out on the results of the
bibliographical research, we used the technique of dense optical flow features
to improve the ability of identifying emotions in children in uncontrolled
environments. From the output of a hybrid model of Convolutional Neural
Network, two intermediary features are fused before being processed by a final
classifier. The proposed architecture was called HybridCNNFusion. Finally, we
present the initial results achieved in the recognition of children's emotions
using a dataset of Brazilian children.