  This paper presents our approach for Facial Expression Intensity Estimation
from videos. It includes two components: i) a representation extractor network
that extracts various emotion descriptors (valence-arousal, action units and
basic expressions) from each videoframe; ii) a RNN that captures temporal
information in the data, followed by a mask layer which enables handling
varying input video lengths through dynamic routing. This approach has been
tested on the Hume-Reaction dataset yielding excellent results.
