The limited capacity to recognize micro-expressions with subtle and rapid motion changes is a long-standing problem that presents a unique challenge for expression recognition systems and even for humans. The problem regarding micro-expression is less covered by research when compared to macro-expression. Nevertheless, micro-expression recognition (MER) is imperative to exploit the full potential of expression recognition for real-world applications. Recent MER systems generally focus on three important issues: overfitting caused by a lack of sufficient training data, the imbalanced distribution of samples, and robust features for improving the accuracy of recognition. In this paper, we provide a comprehensive survey on MER, including datasets and algorithms that provide insights into these intrinsic problems. First, we introduce the available datasets that are widely used in the literature. We then describe the pre-processing in the standard pipeline of an MER system. For the state of the art in MER, we divide the existing novel algorithms into 6 different tasks according to the type of classes and evaluation protocols. Detailed experiment settings and competitive performances for those 6 tasks are summarized in this section. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust MER systems.

识别具有微妙和快速运动变化的微表达的能力有限是一个长期存在的问题，它对表达识别系统甚至人类提出了独特的挑战。与宏观表达相比，关于微观表达的问题很少被研究覆盖。然而，微表达识别（MER）必须充分发挥表达识别在现实应用中的全部潜力。最近的MER系统通常集中在三个重要问题上：由于缺少足够的训练数据而导致的过拟合，样本分布不均以及提高识别精度的强大功能。在本文中，我们对MER进行了全面的调查，包括对这些内在问题有深刻见解的数据集和算法。第一，我们介绍了文献中广泛使用的可用数据集。然后，我们描述MER系统的标准管道中的预处理。对于MER的最新技术，我们根据类的类型和评估协议将现有的新颖算法分为6个不同的任务。本节概述了这6个任务的详细实验设置和竞争表现。最后，我们回顾了该领域尚存的挑战和相应的机遇，以及强大的MER系统设计的未来方向。本节概述了这6个任务的详细实验设置和竞争表现。最后，我们回顾了该领域尚存的挑战和相应的机遇，以及强大的MER系统设计的未来方向。本节概述了这6个任务的详细实验设置和竞争表现。最后，我们回顾了该领域尚存的挑战和相应的机遇，以及强大的MER系统设计的未来方向。