  Facial expression recognition has gained significance as a means of imparting
social robots with the capacity to discern the emotional states of users. The
use of social robotics includes a variety of settings, including homes, nursing
homes or daycare centers, serving to a wide range of users. Remarkable
performance has been achieved by deep learning approaches, however, its direct
use for recognizing facial expressions in individuals with intellectual
disabilities has not been yet studied in the literature, to the best of our
knowledge. To address this objective, we train a set of 12 convolutional neural
networks in different approaches, including an ensemble of datasets without
individuals with intellectual disabilities and a dataset featuring such
individuals. Our examination of the outcomes, both the performance and the
important image regions for the models, reveals significant distinctions in
facial expressions between individuals with and without intellectual
disabilities, as well as among individuals with intellectual disabilities.
Remarkably, our findings show the need of facial expression recognition within
this population through tailored user-specific training methodologies, which
enable the models to effectively address the unique expressions of each user.
