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

Recent research has underscored the increasing preference of users for human-like interactions with machines. Consequently, facial expression recognition has gained significance as a means of imparting social robots with the capacity to discern the emotional states of users. In this investigation, we assess the suitability of deep learning approaches, known for their remarkable performance in this domain, for recognizing facial expressions in individuals with intellectual disabilities, which has not been yet studied in the literature, to the best of our knowledge. To address this objective, we train a set of twelve distinct 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 achieved by the various models under distinct training conditions, coupled with a comprehensive analysis of critical facial regions during expression recognition facilitated by explainable artificial intelligence techniques, revealed significant distinctions in facial expressions between individuals with and without intellectual disabilities, as well as among individuals with intellectual disabilities. Remarkably, our findings demonstrate the feasibility 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.