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Micro-Expression Recognition (MER) is a challenging task as the subtlechanges occur over different action regions of a face. Changes in facial actionregions are formed as Action Units (AUs), and AUs in micro-expressions can beseen as the actors in cooperative group activities. In this paper, we propose anovel deep neural network model for objective class-based MER, whichsimultaneously detects AUs and aggregates AU-level features intomicro-expression-level representation through Graph Convolutional Networks(GCN). Specifically, we propose two new strategies in our AU detection modulefor more effective AU feature learning: the attention mechanism and thebalanced detection loss function. With those two strategies, features arelearned for all the AUs in a unified model, eliminating the error-prunelandmark 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 amicro-expression-level feature representation. Extensive experiments on twotasks in MEGC 2018 show that our approach significantly outperforms the currentstate-of-the-arts in MER. Additionally, we also report our single model-basedmicro-expression AU detection results.

Objective Class-based Micro-Expression Recognition through Simultaneous Action Unit Detection and Feature Aggregation