  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.
