Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms
Xianye Ben, Yi Ren, Junping Zhang, Su-Jing Wang, Kidiyo, Kpalma, Weixiao Meng, Yong-Jin Liu

TL;DR
This survey comprehensively reviews video-based micro-expression analysis, covering datasets, features, algorithms, and applications, and introduces a new dataset to advance research in this challenging field.
Contribution
It provides a systematic overview of micro-expression analysis, introduces the MMEW dataset, and offers unified comparisons of state-of-the-art methods.
Findings
Introduction of the MMEW dataset with more samples and labels
Unified comparison of methods on CAS(ME)2, MMEW, and SAMM datasets
Discussion of key challenges and future directions in micro-expression analysis
Abstract
Unlike the conventional facial expressions, micro-expressions are involuntary and transient facial expressions capable of revealing the genuine emotions that people attempt to hide. Therefore, they can provide important information in a broad range of applications such as lie detection, criminal detection, etc. Since micro-expressions are transient and of low intensity, however, their detection and recognition is difficult and relies heavily on expert experiences. Due to its intrinsic particularity and complexity, video-based micro-expression analysis is attractive but challenging, and has recently become an active area of research. Although there have been numerous developments in this area, thus far there has been no comprehensive survey that provides researchers with a systematic overview of these developments with a unified evaluation. Accordingly, in this survey paper, we first…
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