  Although much progress has been made in the facial expression analysis field,
facial occlusions are still challenging. The main innovation brought by this
contribution consists in exploiting the specificities of facial movement
propagation for recognizing expressions in presence of important occlusions.
The movement induced by an expression extends beyond the movement epicenter.
Thus, the movement occurring in an occluded region propagates towards
neighboring visible regions. In presence of occlusions, per expression, we
compute the importance of each unoccluded facial region and we construct
adapted facial frameworks that boost the performance of per expression binary
classifier. The output of each expression-dependant binary classifier is then
aggregated and fed into a fusion process that aims constructing, per occlusion,
a unique model that recognizes all the facial expressions considered. The
evaluations highlight the robustness of this approach in presence of
significant facial occlusions.
