CMED: A Child Micro-Expression Dataset
This addresses the problem of assisting psychotherapists in therapy by providing a dataset for child micro-expression detection, which is incremental as it extends existing adult-focused research to children.
The study tackled the lack of research on child micro-expressions by compiling the first spontaneous child micro-expression dataset captured in the wild, enabling exploration of differences from adults and establishing baselines for automated spotting and 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.