首个儿童微表情数据集，助力心理治疗中情绪识别。
CMED: A Child Micro-Expression Dataset
- 采集真实场景下儿童自发微表情视频，构建首个儿童微表情数据集。
- 对比成人与儿童微表情差异，发现儿童表情更短暂且不规则。
- 提供自动化检测基线，适合心理学与计算机交叉研究者使用。
微表情是难以掩饰的短暂情绪表现，对儿童的情绪识别在心理治疗中具有重要意义。然而，现有微表情研究多集中于成人，而儿童的表情特征与成人存在显著差异。由于儿童面部表情不可预测、难控制，缺乏针对儿童的微表情数据集成为研究瓶颈。本研究首次构建了基于真实场景的儿童微表情视频数据集（CMED），通过视频会议软件采集，涵盖自发性微表情。该数据集使我们能够分析成人与儿童微表情的关键差异。同时，本研究采用三种方法（手工特征与学习型方法）建立儿童微表情自动识别的基准模型，为后续研究提供基础。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。