Home | Publications | SRR+26a

Do Depressive Facial Patterns Transfer Across Cultures and Contexts? Evidence From a German RCT and E-DAIC

MCML Authors

Link to Profile Björn Eskofier

Björn Eskofier

Prof. Dr.

Core PI

Artificial Intelligence in Medicine

Abstract

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.

inproceedings SRR+26a

AI4Mental @KDD 2026

International Workshop on AI for Cognitive and Mental Health Support at the 32nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Jeju Island, Republic of Korea, Aug 09-13, 2026.

Authors

M. Sadeghi • R. Richer • L. H. Rupp • L. Schindler-Gmelch • M. Keinert • F. Rahimi • M. Hager • B. Egger • M. Berking • B. M. Eskofier

Links

PDF GitHub

Research Area

 C1 | Medicine

BibTeXKey: SRR+26a

#p-eskofier
Back to Top