跨文化面部抑郁特征能否通用？实验发现被动观察时效果最好。
Do Depressive Facial Patterns Transfer Across Cultures and Contexts? Evidence from a German RCT and E-DAIC
- 用双语数据集对比跨文化面部动作预测抑郁
- 被动状态下的模型跨库预测准确率达AUC 0.70
- 适合做心理筛查的轻量级算法研究者参考
从面部动态自动评估抑郁症具有大规模心理健康监测的潜力，但所学生物标志物在不同数据集间的泛化能力仍存挑战。本研究系统性地开展双向迁移实验，结合德国的EmpkinS-EKSpression随机对照试验（N=256，SCID-5-CV诊断）与扩展压力分析访谈语料库（E-DAIC；N=275，半结构化临床访谈），基于面部动作单元、头部姿态和注视方向预测抑郁严重程度及二分类诊断状态。跨语料库二分类表现优于连续型PHQ-8严重度回归，正向迁移达AUC=0.70。回归迁移受功能情境一致性调控：被动观察阶段模型最具可迁移性，而主动情绪调节阶段则产生更强的组内信号。结果表明功能情境对齐是跨语料库泛化的首要决定因素，被动诱发情境在组内敏感性与跨库鲁棒性间取得最佳平衡。
原文摘要 · Abstract (English)
Automated assessment of depression from facial dynamics holds promise for scalable mental health monitoring, yet cross-corpus generalization of learned biomarkers remains an open challenge. We present a systematic bidirectional transfer study pairing the EmpkinS-EKSpression randomized controlled trial (RCT; N = 256, SCID-5-CV diagnoses) with the Extended Distress Analysis Interview Corpus (E-DAIC; N = 275, semi-structured clinical interviews), predicting depression severity and binary diagnostic status from facial action units, head pose, and gaze. Cross-corpus binary classification proves more robust than continuous PHQ-8 severity regression, with forward transfer achieving AUC = 0.70. Regression transfer is governed by functional context alignment: passive observation phases yield the most transferable models, while active emotion regulation phases elicit stronger within-corpus signals. These findings establish functional context alignment as the primary determinant of cross-corpus generalization, with passive elicitation contexts offering the best trade-off between within-corpus sensitivity and cross-corpus robustness.
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