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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.21690 |
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| _version_ | 1866912297560047616 |
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| author | Nikin~Matharaarachchi Pasha, Muhammad~Fermi Sonya~Coleman PengWong, Kah |
| author_facet | Nikin~Matharaarachchi Pasha, Muhammad~Fermi Sonya~Coleman PengWong, Kah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21690 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CMED: A Child Micro-Expression Dataset Nikin~Matharaarachchi Pasha, Muhammad~Fermi Sonya~Coleman PengWong, Kah Computer Vision and Pattern Recognition 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. |
| title | CMED: A Child Micro-Expression Dataset |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.21690 |