One of a kind: inferring personality impressions in meetings

We present an analysis on personality prediction in small groups based on trait attributes from external observers. We use a rich set of automatically extracted audio-visual nonverbal features, including speaking turn, prosodic, visual activity, and visual focus of attention features. We also investigate whether the thin sliced impressions of external observers generalize to the whole meeting in the personality prediction task. Using ridge regression, we have analyzed both the regression and classification performance of personality prediction. Our experiments show that the extraversion trait can be predicted with high accuracy in a binary classification task and visual activity features give higher accuracies than audio ones. The highest accuracy for the extraversion trait, is 75\%, obtained with a combination of audio-visual features. Openness to experience trait also has a significant accuracy, only when the whole meeting is used as the unit of processing.