Facial Expressions Analysis Under Occlusions Based on Specificities of Facial Motion Propagation
This work addresses the problem of accurate facial expression analysis for applications like human-computer interaction in scenarios with occlusions, representing an incremental improvement over existing methods.
The paper tackled facial expression recognition under significant occlusions by exploiting facial motion propagation to adaptively weight visible regions and construct expression-specific classifiers, achieving robust performance as highlighted in evaluations.
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.