论文标题
微表达分析中的数据泄漏和评估问题
Data Leakage and Evaluation Issues in Micro-Expression Analysis
论文作者
论文摘要
由于各种潜在的应用，最近提高了微表达的利息。但是，任务很困难，因为它融合了计算机视觉，机器学习和情感科学领域的许多挑战。由于微表达的自发和微妙的特征，可用的培训和测试数据受到限制，这使得评估复杂。我们表明，数据泄漏和分散的评估方案是微表达文献中的问题。我们发现，修复数据泄漏可以大大降低模型性能，在某些情况下，即使使模型的性能类似于随机分类器。为此，我们经历了常见的陷阱，使用具有超过2000个微表达样本的面部动作单元提出了一种新的标准化评估协议，并提供了以标准化方式实现评估协议的开源库。代码在\ url {https://github.com/tvaranka/meb}中公开可用。
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}.
