  Recognizing the expressions of partially occluded faces is a challenging
computer vision problem. Previous expression recognition methods, either
overlooked this issue or resolved it using extreme assumptions. Motivated by
the fact that the human visual system is adept at ignoring the occlusion and
focus on non-occluded facial areas, we propose a landmark-guided attention
branch to find and discard corrupted features from occluded regions so that
they are not used for recognition. An attention map is first generated to
indicate if a specific facial part is occluded and guide our model to attend to
non-occluded regions. To further improve robustness, we propose a facial region
branch to partition the feature maps into non-overlapping facial blocks and
task each block to predict the expression independently. This results in more
diverse and discriminative features, enabling the expression recognition system
to recover even though the face is partially occluded. Depending on the
synergistic effects of the two branches, our occlusion-adaptive deep network
significantly outperforms state-of-the-art methods on two challenging
in-the-wild benchmark datasets and three real-world occluded expression
datasets.
