# CMED: A Child Micro-Expression Dataset

arXiv: https://arxiv.org/abs/2503.21690 · HTML: https://mlsanity.com/paper/2503.21690/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 44 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition](https://mlsanity.com/paper/1902.03634/) (2019)
- [Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods](https://mlsanity.com/paper/1511.00423/) (2015)
- [MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network](https://mlsanity.com/paper/2004.08915/) (2020)
- [The politics of deceptive borders: 'biomarkers of deceit' and the case of iBorderCtrl](https://mlsanity.com/paper/1911.09156/) (2019)

## Abstract (arXiv)

Micro-expressions are short bursts of emotion that are difficult to hide. Their detection in children is an important cue to assist psychotherapists in conducting better therapy. However, existing research on the detection of micro-expressions has focused on adults, whose expressions differ in their characteristics from those of children. The lack of research is a direct consequence of the lack of a child-based micro-expressions dataset as it is much more challenging to capture children's facial expressions due to the lack of predictability and controllability. This study compiles a dataset of spontaneous child micro-expression videos, the first of its kind, to the best of the authors knowledge. The dataset is captured in the wild using video conferencing software. This dataset enables us to then explore key features and differences between adult and child micro-expressions. This study also establishes a baseline for the automated spotting and recognition of micro-expressions in children using three approaches comprising of hand-created and learning-based approaches.