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.

Assessing Gender Bias in Predictive Algorithms using eXplainable AI

Predictive algorithms have a powerful potential to offer benefits in areas as varied as medicine or education. However, these algorithms and the data they use are built by humans, consequently, they can inherit the bias and prejudices present in humans. The outcomes can systematically repeat errors that create unfair results, which can even lead to situations of discrimination (e.g. gender, social or racial). In order to illustrate how important is to count with a diverse training dataset to avoid bias, we manipulate a well-known facial expression recognition dataset to explore gender bias and discuss its implications.

Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images

We address the use of selfie ocular images captured with smartphones to estimate age and gender. Partial face occlusion has become an issue due to the mandatory use of face masks. Also, the use of mobile devices has exploded, with the pandemic further accelerating the migration to digital services. However, state-of-the-art solutions in related tasks such as identity or expression recognition employ large Convolutional Neural Networks, whose use in mobile devices is infeasible due to hardware limitations and size restrictions of downloadable applications. To counteract this, we adapt two existing lightweight CNNs proposed in the context of the ImageNet Challenge, and two additional architectures proposed for mobile face recognition. Since datasets for soft-biometrics prediction using selfie images are limited, we counteract over-fitting by using networks pre-trained on ImageNet. Furthermore, some networks are further pre-trained for face recognition, for which very large training databases are available. Since both tasks employ similar input data, we hypothesize that such strategy can be beneficial for soft-biometrics estimation. A comprehensive study of the effects of different pre-training over the employed architectures is carried out, showing that, in most cases, a better accuracy is obtained after the networks have been fine-tuned for face recognition.