Realism of the face lies in skin and eyes: Evidence from virtual and human agents

Abstract

Despite advancements in computer graphics and artificial intelligence, it remains unclear which aspects of intelligent virtual agents (IVAs) make them identifiable as human-like agents. In three experiments and a computational study, we investigated which specific facial features in static IVAs contribute to judging them human-like. In Experiment 1, participants were presented with facial images of state-of-the-art IVAs and humans and asked to rate these stimuli on human-likeness. The results showed that IVAs were judged less human-like compared to photographic images of humans, which led to the hypothesis that the discrepancy in human-likeness was driven by skin and eye reflectance. A follow-up computational analysis confirmed this hypothesis, showing that the faces of IVAs had smoother skin and a reduced number of corneal reflections than human faces. In Experiment 2, we validated these findings by systematically manipulating the appearance of skin and eyes in a set of human photographs, including both female and male faces as well as four different races. Participants indicated as quickly as possible whether the image depicted a real human face or not. The results showed that smoothening the skin and removing corneal reflections affected the perception of human-likeness when quick perceptual decisions needed to be made. Finally, in Experiment 3, we combined the images of IVA faces and those of humans, unaltered and altered, and asked participants to rate them on human-likeness. The results confirmed the causal role of both features for attributing human-likeness. Both skin and eye reflectance worked in tandem in driving judgements regarding the extent to which the face was perceived human-like in both IVAs and humans. These findings are of relevance to computer graphics artists and psychology researchers alike in drawing attention to those facial characteristics that increase realism in IVAs.