  We explore retrieving character-focused video frames as candidates for being
video thumbnails. To evaluate each frame of the video based on the character(s)
present in it, characters (faces) are evaluated in two aspects:
Facial-expression: We train a CNN model to measure whether a face has an
acceptable facial expression for being in a video thumbnail. This model is
trained to distinguish faces extracted from artworks/thumbnails, from faces
extracted from random frames of videos. Prominence and interactions:
Character(s) in the thumbnail should be important character(s) in the video, to
prevent the algorithm from suggesting non-representative frames as candidates.
We use face clustering to identify the characters in the video, and form a
graph in which the prominence (frequency of appearance) of the character(s),
and their interactions (co-occurrence) are captured. We use this graph to infer
the relevance of the characters present in each candidate frame. Once every
face is scored based on the two criteria above, we infer frame level scores by
combining the scores for all the faces within a frame.
