Estimating Perceived Comfort in Virtual Humans based on Spatial and Spectral Entropy

Nowadays, we are increasingly exposed to applications with conversational agents or virtual humans. In the psychology literature, the perception of human faces is a research area well studied. In past years, many works have investigated human perception concerning virtual humans. The sense of discomfort perceived in certain virtual characters, discussed in Uncanny Valley (UV) theory, can be a key factor in our perceptual and cognitive discrimination. Understanding how this process happens is essential to avoid it in the process of modeling virtual humans. This paper investigates the relationship between images features and the comfort that human beings can feel about the animated characters created using Computer Graphics (CG). We introduce the CCS (Computed Comfort Score) metric to estimate the probable comfort/discomfort value that a particular virtual human face can generate in the subjects. We used local spatial and spectral entropy to extract features and show their relevance to the subjects' evaluation. A model using Support Vector Regression (SVR) is proposed to compute the CCS. The results indicated approximately an accuracy of 80% for the tested images when compared with the perceptual data.