  Micro-expressions (MEs) are involuntary movements revealing people's hidden
feelings, which has attracted numerous interests for its objectivity in emotion
detection. However, despite its wide applications in various scenarios,
micro-expression recognition (MER) remains a challenging problem in real life
due to three reasons, including (i) data-level: lack of data and imbalanced
classes, (ii) feature-level: subtle, rapid changing, and complex features of
MEs, and (iii) decision-making-level: impact of individual differences. To
address these issues, we propose a dual-branch meta-auxiliary learning method,
called LightmanNet, for fast and robust micro-expression recognition.
Specifically, LightmanNet learns general MER knowledge from limited data
through a dual-branch bi-level optimization process: (i) In the first level, it
obtains task-specific MER knowledge by learning in two branches, where the
first branch is for learning MER features via primary MER tasks, while the
other branch is for guiding the model obtain discriminative features via
auxiliary tasks, i.e., image alignment between micro-expressions and
macro-expressions since their resemblance in both spatial and temporal
behavioral patterns. The two branches of learning jointly constrain the model
of learning meaningful task-specific MER knowledge while avoiding learning
noise or superficial connections between MEs and emotions that may damage its
generalization ability. (ii) In the second level, LightmanNet further refines
the learned task-specific knowledge, improving model generalization and
efficiency. Extensive experiments on various benchmark datasets demonstrate the
superior robustness and efficiency of LightmanNet.
