  Micro-Expression Recognition (MER) is a challenging task as the subtle
changes occur over different action regions of a face. Changes in facial action
regions are formed as Action Units (AUs), and AUs in micro-expressions can be
seen as the actors in cooperative group activities. In this paper, we propose a
novel deep neural network model for objective class-based MER, which
simultaneously detects AUs and aggregates AU-level features into
micro-expression-level representation through Graph Convolutional Networks
(GCN). Specifically, we propose two new strategies in our AU detection module
for more effective AU feature learning: the attention mechanism and the
balanced detection loss function. With those two strategies, features are
learned for all the AUs in a unified model, eliminating the error-prune
landmark detection process and tedious separate training for each AU. Moreover,
our model incorporates a tailored objective class-based AU knowledge-graph,
which facilitates the GCN to aggregate the AU-level features into a
micro-expression-level feature representation. Extensive experiments on two
tasks in MEGC 2018 show that our approach significantly outperforms the current
state-of-the-arts in MER. Additionally, we also report our single model-based
micro-expression AU detection results.
