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

arXiv: https://arxiv.org/abs/1805.02397 · HTML: https://mlsanity.com/paper/1805.02397/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2018.

## Built on by (most cited first, 9 of 9)

- [Deep Learning for Micro-expression Recognition: A Survey](https://mlsanity.com/paper/2107.02823/) (2021)
- [A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions](https://mlsanity.com/paper/2305.07665/) (2023)
- [Meta-Auxiliary Learning for Micro-Expression Recognition](https://mlsanity.com/paper/2404.12024/) (2024)
- [Deep Insights of Learning based Micro Expression Recognition: A Perspective on Promises, Challenges and Research Needs](https://mlsanity.com/paper/2210.04935/) (2022)
- [Automatic Micro-Expression Apex Frame Spotting using Local Binary Pattern from Six Intersection Planes](https://mlsanity.com/paper/2104.02149/) (2021)
- [MELDAE: A Framework for Micro-Expression Spotting, Detection, and Automatic Evaluation in In-the-Wild Conversational Scenes](https://mlsanity.com/paper/2510.22575/) (2025)
- [A Dynamic 3D Spontaneous Micro-expression Database: Establishment and Evaluation](https://mlsanity.com/paper/2108.00166/) (2021)
- [Enhancing Lie Detection Accuracy: A Comparative Study of Classic ML, CNN, and GCN Models using Audio-Visual Features](https://mlsanity.com/paper/2411.08885/) (2024)
- [EPIR: An Efficient Patch Tokenization, Integration and Representation Framework for Micro-expression Recognition](https://mlsanity.com/paper/2604.08106/) (2026)

## Abstract (arXiv)

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.