  Optical flow techniques are becoming increasingly performant and robust when
estimating motion in a scene, but their performance has yet to be proven in the
area of facial expression recognition. In this work, a variety of optical flow
approaches are evaluated across multiple facial expression datasets, so as to
provide a consistent performance evaluation. The aim of this work is not to
propose a new expression recognition technique, but to understand better the
adequacy of existing state-of-the art optical flow for encoding facial motion
in the context of facial expression recognition. Our evaluations highlight the
fact that motion approximation methods used to overcome motion discontinuities
have a significant impact when optical flows are used to characterize facial
expressions.
