  Spatial-temporal feature learning is of vital importance for video emotion
recognition. Previous deep network structures often focused on macro-motion
which extends over long time scales, e.g., on the order of seconds. We believe
integrating structures capturing information about both micro- and macro-motion
will benefit emotion prediction, because human perceive both micro- and
macro-expressions. In this paper, we propose to combine micro- and macro-motion
features to improve video emotion recognition with a two-stream recurrent
network, named MIMAMO (Micro-Macro-Motion) Net. Specifically, smaller and
shorter micro-motions are analyzed by a two-stream network, while larger and
more sustained macro-motions can be well captured by a subsequent recurrent
network. Assigning specific interpretations to the roles of different parts of
the network enables us to make choice of parameters based on prior knowledge:
choices that turn out to be optimal. One of the important innovations in our
model is the use of interframe phase differences rather than optical flow as
input to the temporal stream. Compared with the optical flow, phase differences
require less computation and are more robust to illumination changes. Our
proposed network achieves state of the art performance on two video emotion
datasets, the OMG emotion dataset and the Aff-Wild dataset. The most
significant gains are for arousal prediction, for which motion information is
intuitively more informative. Source code is available at
https://github.com/wtomin/MIMAMO-Net.

Computer Vision and Pattern Recognition