Continuous prediction of perceived traits and social dimensions in space and time

Developing automatic personality predictors requires generating reliable annotations, i.e., ground truth. To date, researchers have relied on the overall ratings provided for a whole video sequence, either obtained by self-assessment or provided by external observers. In this paper, we propose a novel personality assessment approach, where we ask external observers to continuously provide ratings along multiple dimensions ranging from 0 to 100 along time, and we generate continuous annotations in space and time. In addition to the widely used Big Five personality dimensions, we introduce three more dimensions that have the potential to gauge the reliability of the perceived social and trait judgements in the context of varying situational interactions between a human subject and virtual characters. Our results demonstrate the viability of the proposed approach and the plausible relationship between the extracted features and perceived trait and social dimensions. Annotations obtained continuously in time and in trait-social dimensional space showed that a number of dimensions appear to be more static and stable over time while other dimensions appear to be more dynamic.