Micro-expressions have drawn increasing interest lately due to various potential applications. The task is, however, difficult as it incorporates many challenges from the fields of computer vision, machine learning and emotional sciences. Due to the spontaneous and subtle characteristics of micro-expressions, the available training and testing data are limited, which make evaluation complex. We show that data leakage and fragmented evaluation protocols are issues among the micro-expression literature. We find that fixing data leaks can drastically reduce model performance, in some cases even making the models perform similarly to a random classifier. To this end, we go through common pitfalls, propose a new standardized evaluation protocol using facial action units with over 2000 micro-expression samples, and provide an open source library that implements the evaluation protocols in a standardized manner. Code is publicly available in \url{https://github.com/tvaranka/meb}.

翻译：微表情因其多样潜在应用而受到日益关注。然而，该任务具有挑战性，因为它融合了计算机视觉、机器学习和情感科学领域的诸多难题。由于微表情的自发性和细微性特征，可用的训练与测试数据有限，这使得评估变得复杂。我们证明，数据泄露和碎片化评估协议是微表情研究中的常见问题。研究发现，修复数据泄露会显著降低模型性能，某些情况下甚至使模型表现与随机分类器相当。为此，我们梳理了常见陷阱，提出了一种基于人脸动作单元的标准化评估协议（涵盖超过2000个微表情样本），并提供了一个以标准化方式实现评估协议的开源库。代码公开于 \url{https://github.com/tvaranka/meb}。