  We present a method that automatically evaluates emotional response from
spontaneous facial activity recorded by a depth camera. The automatic
evaluation of emotional response, or affect, is a fascinating challenge with
many applications, including human-computer interaction, media tagging and
human affect prediction. Our approach in addressing this problem is based on
the inferred activity of facial muscles over time, as captured by a depth
camera recording an individual's facial activity. Our contribution is two-fold:
First, we constructed a database of publicly available short video clips, which
elicit a strong emotional response in a consistent manner across different
individuals. Each video was tagged by its characteristic emotional response
along 4 scales: \emph{Valence, Arousal, Likability} and \emph{Rewatch} (the
desire to watch again). The second contribution is a two-step prediction
method, based on learning, which was trained and tested using this database of
tagged video clips. Our method was able to successfully predict the
aforementioned 4 dimensional representation of affect, as well as to identify
the period of strongest emotional response in the viewing recordings, in a
method that is blind to the video clip being watch, revealing a significantly
high agreement between the recordings of independent viewers.
