Hybrid Models for Facial Emotion Recognition in Children

TL;DR

This paper introduces HybridCNNFusion, a hybrid deep learning model that improves emotion recognition in children for remote therapy, utilizing dense optical flow features and a fusion approach, with initial promising results on a Brazilian dataset.

Contribution

The paper proposes a novel hybrid CNN model with feature fusion for enhanced emotion recognition in children in uncontrolled environments.

Findings

Improved accuracy in recognizing children's emotions using dense optical flow features.

Effective fusion of intermediary features enhances classification performance.

Initial results demonstrate feasibility on a dataset of Brazilian children.

Abstract

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.