  In recent years, extensive research has emerged in affective computing on
topics like automatic emotion recognition and determining the signals that
characterize individual emotions. Much less studied, however, is
expressiveness, or the extent to which someone shows any feeling or emotion.
Expressiveness is related to personality and mental health and plays a crucial
role in social interaction. As such, the ability to automatically detect or
predict expressiveness can facilitate significant advancements in areas ranging
from psychiatric care to artificial social intelligence. Motivated by these
potential applications, we present an extension of the BP4D+ dataset with human
ratings of expressiveness and develop methods for (1) automatically predicting
expressiveness from visual data and (2) defining relationships between
interpretable visual signals and expressiveness. In addition, we study the
emotional context in which expressiveness occurs and hypothesize that different
sets of signals are indicative of expressiveness in different contexts (e.g.,
in response to surprise or in response to pain). Analysis of our statistical
models confirms our hypothesis. Consequently, by looking at expressiveness
separately in distinct emotional contexts, our predictive models show
significant improvements over baselines and achieve comparable results to human
performance in terms of correlation with the ground truth.
