  Interest makes one hold her attention on the object of interest. Automatic
recognition of interest has numerous applications in human-computer
interaction. In this paper, we study the facial expressions associated with
interest and its underlying and closely related components, namely, curiosity,
coping potential, novelty and complexity. To this end, we conducted an
experiment in which participants watched images and micro-videos while a
front-facing camera recorded their expressions. After watching each item they
self-reported their level of interest, curiosity, coping potential and
perceived novelty and complexity. Using an automated method, we tracked facial
action units (AU) and studied the relationship between the presence of facial
movements with interest and its related components. We then tracked the facial
landmarks, e.g., corners of lips, and extracted features from each response. We
trained random forests regression models to detect the level of interest,
curiosity, and appraisals. We found a large difference between the way people
report and react to interesting visual content. The expressions in response to
images and micro-videos were not always pronounced depending on the
participants. This makes the direct detection of interest from facial
expressions a challenging problem. With this work, for the first time, we
demonstrate the feasibility of detecting cognitive appraisals from facial
expressions which will open the door for appraisal-driven emotion recognition
methods.
