communication, behaviors, and interests [4; 6; 7]. This condition may often lead to more severe health problems attributed to isolation and unemployment (or reduced/under employment), which can result in depression and anxiety [4]. To counteract these problems, group or single therapies are devised for each level of ASD. A closed-loop emotional feedback system is in development to immerse subjects in a virtual world and have them take part in different gamified scenarios and tasks designed to stimulate emotional responses as part of therapy. This system also aims to assist clinicians by providing vital information to monitor progress and adapt the therapy level, assuring better quality and more personalized care. Such digital therapeutic intervention systems have the potential to assist individuals with ASD to cope better in different social environments [8; 9; 10]. Defining an emotion is a topic of great debate [11]. In this work, an emotion is considered as defined by the framework of a component process model, a sequence of triggers interrelated to changes in organism states in response to an external or internal stimulus [11]. Over the years, the understanding of emotional stimuli, in particular their influence and role in clinical settings, has evolved. Emotional stimuli, more specifically those identified as threats, have significant influence on selective attention, processing prioritization, and Pavlovian response [12; 13]. However, an interesting perspective has emerged in recent studies, that reactions to such stimuli are aligned with personal objectives. This perspective suggests a context-dependent response of facial expressions [14; 15]. To ensure efficient emotional feedback of the proposed system, an effective model for the recognition of expressions must be developed. Identifying emotions is often a difficult task given the numerous ways a person can express themselves [16]. However, emotions are often perceived through either facial expressions (55%); speech and voice patterns (35%); or physiological signals (10%) [17]. This study concentrates on the use of facial expressions as a basis for emotion recognition. It is also worth noting that previous research [18; 19] has shed light on the importance of whole-body expressions in relating to the emotional states of others, such as by recognizing emotions when the face is occluded and conveying action intentions. The prevalence of studies [20; 21; 22; 23; 24; 25] on non-verbal emotional cues highlights the growing interest in the evolving field of recognizing emotions to bridge the gap in human-machine interactions. Extracting information from facial expressions is achieved via two methods: (1) image-based; and (2) geometric-based. This study uses a geometric-based approach, where facial point landmarks are extracted and used in combination with a graph convolution network to provide a robust representation of emotion state data. The use of facial landmarks provides a more interrelated and holistic approach to the identification of emotions from facial expressions. In [16], the use of facial landmark locations incorporated in the classification process with a unique loss function revealed promising results when tested on distinct emotional datasets. The work of [26] highlighted the use of facial landmarks extracted from a Kinect 3D device to identify action units (AUs) based on the representation from the facial action coding system (FACS) [27]. The use of the AUs allowed the classification model performance to reach 96% accuracy on the Karolinska directed emotional faces (KDEF) [28] dataset. A combination of facial landmark localization, with 68 facial landmarks, and physiological signals was studied in [29]. The implemented model was able to effectively classify six emotional classes with an accuracy of 86.94% on the gathered dataset. In [30], key facial landmarks were selected and used for geometric analysis of facial gestures on three different datasets using machine learning models for classification.