Micro-expression recognition (MER) has drawn increasing attention in recent
years due to its potential applications in intelligent medical and lie
detection. However, the shortage of annotated data has been the major obstacle
to further improve deep-learning based MER methods. Intuitively, utilizing
sufficient macro-expression data to promote MER performance seems to be a
feasible solution. However, the facial patterns of macro-expressions and
micro-expressions are significantly different, which makes naive transfer
learning methods difficult to deploy directly. To tacle this issue, we propose
a generalized transfer learning paradigm, called \textbf{MA}cro-expression
\textbf{TO} \textbf{MI}cro-expression (MA2MI). Under our paradigm, networks can
learns the ability to represent subtle facial movement by reconstructing future
frames. In addition, we also propose a two-branch micro-action network
(MIACNet) to decouple facial position features and facial action features,
which can help the network more accurately locate facial action locations.
Extensive experiments on three popular MER benchmarks demonstrate the
superiority of our method.

微表情识别是近年来备受关注的研究领域，在智能医学和骗术检测等方面具有潜在应用。然而，缺乏标注数据一直以来都是加强基于深度学习的微表情识别方法的主要障碍之一。本文提出了一种广义的转移学习范式，称为宏观表情到微表情（MA2MI）。在该范式下，网络可以通过重建未来帧来学习表示微妙的面部运动。此外，本文还提出了一种两支路微动作网络（MIACNet），用于解耦面部位置特征和面部动作特征，帮助网络更准确地定位面部动作位置。在三个常用的微表情识别基准上进行的大量实验证明了我们方法的优越性。