Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms

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 highlight the key differences between macro- and micro-expressions, then use these differences to guide our research survey of video-based micro-expression analysis in a cascaded structure, encompassing the neuropsychological basis, datasets, features, spotting algorithms, recognition algorithms, applications and evaluation of state-of-the-art approaches. For each aspect, the basic techniques, advanced developments and major challenges are addressed and discussed. Furthermore, after considering the limitations of existing micro-expression datasets, we present and release a new dataset - called micro-and-macro expression warehouse (MMEW) - containing more video samples and more labeled emotion types. We then perform a unified comparison of representative methods on CAS(ME)2 for spotting, and on MMEW and SAMM for recognition, respectively. Finally, some potential future research directions are explored and outlined.