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.

翻译：微表情（MEs）是揭示人类隐藏情感的不自主运动，因其在情感检测中的客观性而吸引了众多研究兴趣。然而，尽管微表情识别（MER）在多种场景中具有广泛应用，但在实际生活中仍面临三大挑战：（i）数据层面：数据匮乏与类别不平衡；（ii）特征层面：微表情特征的细微、快速变化及复杂性；（iii）决策层面：个体差异的影响。为解决这些问题，我们提出了一种名为LightmanNet的双分支元辅助学习方法，用于实现快速鲁棒的微表情识别。具体而言，LightmanNet通过双分支双层优化过程从有限数据中学习通用的MER知识：（i）在第一层，通过两个分支学习获取任务特定的MER知识：第一分支通过主MER任务学习微表情特征，另一分支通过辅助任务（即利用微表情与宏表情在时空行为模式上的相似性进行图像对齐）引导模型获得判别性特征。两个分支的联合学习约束模型习得有意义的任务特定MER知识，同时避免学习噪声或微表情与情感间的浅层关联，从而防止损害模型泛化能力。（ii）在第二层，LightmanNet进一步优化已习得的任务特定知识，提升模型泛化性与效率。在多个基准数据集上的大量实验表明，LightmanNet具有卓越的鲁棒性和效率。