构建跨设备微表情数据集，支持真实压力场景下的情绪识别
CDER-SME: A Cross-Device Event-RGB Micro-Expression Dataset under Multi-Level Stress Induction
- 设计多层级压力诱导框架，采集自发情绪泄露的事件-图像数据
- 包含92人1963样本，790对事件-图像数据，210组高精度配准对
- 无需同轴校准，适合实际部署的跨设备情绪识别研究
真实场景中的微表情识别需要高时间敏感性和生态有效性，但现有基准大多局限于实验室控制环境和固定硬件传感。我们提出CDER-SME，一个在认知与社会双重压力诱导下收集的跨设备事件-图像（Event-RGB）数据集，以激发自发情绪反应。为实现可复现的独立传感器采集，我们提供无硬件依赖的时间同步与基于关键点的空间配准流程。数据集采用三层结构，涵盖92名受试者和1,963个专家标注样本（动作单元与情绪），其中包括790对事件-图像数据和210组高保真对齐样本。我们报告了一个可复现的多模态基线，发现跨模态融合性能优于单模态方法，验证了事件动态与RGB线索的互补性。通过消除同轴校准需求，CDER-SME为跨设备对齐与实际应用中的事件-图像微表情识别提供了实用基准。
原文摘要 · Abstract (English)
Micro-expression recognition (MER) in realistic scenarios demands high temporal sensitivity and ecological validity, yet existing benchmarks are largely constrained to laboratory-controlled settings and rigid hardware-coupled sensing. We introduce CDER-SME, a cross-device Event-RGB dataset collected under a multi-level stress induction framework (cognitive and social) to elicit spontaneous emotional leakage. To enable reproducible acquisition with independent, decoupled sensors, we provide a hardware-agnostic alignment pipeline for temporal synchronization and landmark-guided spatial registration. CDER-SME adopts a three-tier structure with 92 subjects and 1,963 expert-annotated samples (Action Units and emotions), including 790 Event-RGB pairs and 210 high-fidelity aligned pairs. We further report a reproducible multimodal baseline, where cross-modal fusion improves performance over single-modality counterparts, supporting the complementarity of event dynamics and RGB cues. By removing the need for coaxial calibration, CDER-SME offers a practical benchmark for cross-device alignment and deployable Event-RGB MER in real-world affective intelligence.
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