Comparative Analysis of Facial Expression Recognition Systems for Evaluating Emotional States in Virtual Humans
Jonas de Araújo Luz, Maria Andréia Formico Rodriguês · 2023
The digital animation process is a complex endeavour requiring professional animators to acquire substantial expertise and technique through years of study and practice. Particularly, facial animation, where virtual humans express specific mental states or emotions with a desire for realism, is further complicated by the “Uncanny Valley” phenomenon. In this context, it is posited that pre-validated facial expressions for certain emotions could serve as references for the novice or inexperienced animators during the facial animation and posing process of their virtual humans using morph targets, also known as blend shapes or shape keys. This research presents a comparative study between two Facial Expression Recognition (FER) systems that employ pre-trained models for facial recognition applied to emotion recognition in virtual humans. Given that these systems were not designed or trained for this particular purpose but for facial recognition in real humans, this study aims to investigate their level of applicability in scenarios where virtual humans are used instead of real humans. This assessment is a critical step towards evaluating the feasibility of integrating FER models as part of a support tool for facial animation and the posing of virtual humans. Through this investigation, this research provides evidence of the reliability of applying the FER library and Deepface systems for emotion recognition in virtual humans, contributing to investigating new ways to enhance the digital animation process and overcoming the inherent complexities of facial animation.