  Micro-expressions are reflections of people's true feelings and motives,
which attract an increasing number of researchers into the study of automatic
facial micro-expression recognition. The short detection window, the subtle
facial muscle movements, and the limited training samples make micro-expression
recognition challenging. To this end, we propose a novel Identity-aware and
Capsule-Enhanced Generative Adversarial Network with graph-based reasoning
(ICE-GAN), introducing micro-expression synthesis as an auxiliary task to
assist recognition. The generator produces synthetic faces with controllable
micro-expressions and identity-aware features, whose long-ranged dependencies
are captured through the graph reasoning module (GRM), and the discriminator
detects the image authenticity and expression classes. Our ICE-GAN was
evaluated on Micro-Expression Grand Challenge 2019 (MEGC2019) with a
significant improvement (12.9%) over the winner and surpassed other
state-of-the-art methods.
