Explainable Facial Expression Recognition for People with Intellectual Disabilities

TL;DR

This paper investigates facial expression recognition for people with intellectual disabilities using neural networks and explainability techniques, aiming to improve transparency and understanding in social robot interactions.

Contribution

It introduces a study applying neural networks and explainability methods to recognize expressions in disabled individuals, comparing focus regions across different populations.

Findings

Neural networks achieved accurate expression recognition.

Explainability techniques revealed different focus regions for disabled and non-disabled images.

The study supports integrating transparent facial recognition in social robots for disabled users.

Abstract

Facial expression recognition plays an important role in human behaviour, communication, and interaction. Recent neural networks have demonstrated to perform well at its automatic recognition, with different explainability techniques available to make them more transparent. In this work, we propose a facial expression recognition study for people with intellectual disabilities that would be integrated into a social robot. We train two well-known neural networks with five databases of facial expressions and test them with two databases containing people with and without intellectual disabilities. Finally, we study in which regions the models focus to perceive a particular expression using two different explainability techniques: LIME and RISE, assessing the differences when used on images containing disabled and non-disabled people.