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Recent research has underscored the increasing preference of users forhuman-like interactions with machines. Consequently, facial expressionrecognition has gained significance as a means of imparting social robots withthe capacity to discern the emotional states of users. In this investigation,we assess the suitability of deep learning approaches, known for theirremarkable performance in this domain, for recognizing facial expressions inindividuals with intellectual disabilities, which has not been yet studied inthe literature, to the best of our knowledge. To address this objective, wetrain a set of twelve distinct convolutional neural networks in differentapproaches, including an ensemble of datasets without individuals withintellectual disabilities and a dataset featuring such individuals. Ourexamination of the outcomes achieved by the various models under distincttraining conditions, coupled with a comprehensive analysis of critical facialregions during expression recognition facilitated by explainable artificialintelligence techniques, revealed significant distinctions in facialexpressions between individuals with and without intellectual disabilities, aswell as among individuals with intellectual disabilities. Remarkably, ourfindings demonstrate the feasibility of facial expression recognition withinthis population through tailored user-specific training methodologies, whichenable the models to effectively address the unique expressions of each user.

Evaluating the Feasibility of Standard Facial Expression Recognition in Individuals with Moderate to Severe Intellectual Disabilities