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.

翻译：面部表情识别作为赋予社交机器人辨别用户情绪状态能力的重要手段，其重要性日益凸显。社交机器人的应用涵盖多种场景，包括家庭、养老院或日托中心，服务于广泛的用户群体。尽管深度学习方法已取得显著性能，但据我们所知，其在智力障碍者面部表情识别中的直接应用尚未在文献中得到研究。为实现这一目标，我们采用不同方法训练了12个卷积神经网络，包括使用不含智力障碍者的数据集集成和包含此类个体的数据集。通过对模型性能及重要图像区域的分析，我们发现智力障碍者与非智力障碍者之间，以及智力障碍者个体之间的面部表情存在显著差异。值得注意的是，我们的研究结果表明，针对该群体需通过定制化的用户特定训练方法实现面部表情识别，从而使模型能够有效处理每位用户的独特表情表达。