  Video facial expression recognition is useful for many applications and
received much interest lately. Although some solutions give really good results
in a controlled environment (no occlusion), recognition in the presence of
partial facial occlusion remains a challenging task. To handle occlusions,
solutions based on the reconstruction of the occluded part of the face have
been proposed. These solutions are mainly based on the texture or the geometry
of the face. However, the similarity of the face movement between different
persons doing the same expression seems to be a real asset for the
reconstruction. In this paper we exploit this asset and propose a new solution
based on an auto-encoder with skip connections to reconstruct the occluded part
of the face in the optical flow domain. To the best of our knowledge, this is
the first proposition to directly reconstruct the movement for facial
expression recognition. We validated our approach in the controlled dataset CK+
on which different occlusions were generated. Our experiments show that the
proposed method reduce significantly the gap, in terms of recognition accuracy,
between occluded and non-occluded situations. We also compare our approach with
existing state-of-the-art solutions. In order to lay the basis of a
reproducible and fair comparison in the future, we also propose a new
experimental protocol that includes occlusion generation and reconstruction
evaluation.
