Facial Emotion Recognition of Virtual Humans with Different Genders, Races, and Ages

Abstract

Research studies suggest that racial and gender stereotypes can influence emotion recognition accuracy both for adults and children. Stereotypical biases have severe consequences in social life but are especially critical in domains such as education and healthcare, where virtual humans have been extending their applications. In this work, we explore potential perceptual differences in the facial emotion recognition accuracy of virtual humans of different genders, races, and ages. We use realistic 3D models of male/female, Black/White, and child/adult characters. Using blendshapes and the Facial Action Coding System, we created videos of the models displaying facial expressions of six universal emotions with varying intensities. We ran an Amazon Mechanical Turk study to collect perceptual data. The results indicate statistically significant main effects of emotion type and intensity on emotion recognition accuracy. Although overall emotion recognition accuracy was similar across model race, gender, and age groups, there were some statistically significant effects across different groups for individual emotion types.