A Review on Facial Micro-Expressions Analysis: Datasets, Features and Metrics

TL;DR

This review paper summarizes recent advances in facial micro-expressions analysis, focusing on datasets, features, metrics, challenges, and future research directions to improve automatic recognition systems.

Contribution

It provides a comprehensive overview of existing datasets, features, and evaluation metrics, highlighting challenges and proposing future research directions in micro-expressions analysis.

Findings

Multiple datasets facilitate research but lack standardization.

Feature extraction methods vary widely across studies.

Evaluation metrics are inconsistent, hindering comparison.

Abstract

Facial micro-expressions are very brief, spontaneous facial expressions that appear on the face of humans when they either deliberately or unconsciously conceal an emotion. Micro-expression has shorter duration than macro-expression, which makes it more challenging for human and machine. Over the past ten years, automatic micro-expressions recognition has attracted increasing attention from researchers in psychology, computer science, security, neuroscience and other related disciplines. The aim of this paper is to provide the insights of automatic micro-expressions and recommendations for future research. There has been a lot of datasets released over the last decade that facilitated the rapid growth in this field. However, comparison across different datasets is difficult due to the inconsistency in experiment protocol, features used and evaluation methods. To address these issues, we review the datasets, features and the performance metrics deployed in the literature. Relevant challenges such as the spatial temporal settings during data collection, emotional classes versus objective classes in data labelling, face regions in data analysis, standardisation of metrics and the requirements for real-world implementation are discussed. We conclude by proposing some promising future directions to advancing micro-expressions research.