The semantic space for motion-captured facial expressions

We cannot not communicate! During our daily lives, we convey information verbally and nonverbally. Most of the affective meaning of a message is transferred with the help of facial expressions, and thereby, when trying to establish a realistic human-like virtual character, we should pay close attention to the animation. Motion capture is one of the most common techniques, but due to the wide range of expressions humans use, the recording time and data needed are vast. To address this problem, we propose the use of semantic spaces as they help in characterizing and positioning expressions by finding a correlation between them. In this paper, we extend prior research by providing the semantic spaces underlying real videos and motion capture data for a total of 62 conversational expressions. Our results highly correlate with previous work, showing that our new expressions were correctly recognized. Moreover, our results can be used in future work to directly project potential new recordings of these 62 expressions on the found spaces.