🤖 AI Summary
Existing depression and anxiety recognition models suffer from limited generalizability in real-world settings due to small-sample constraints and insufficient modeling of clinically relevant behavioral biomarkers.
Method: We introduce the first large-scale, multimodal dataset for adolescent mental health (n = 11,427), integrating standardized facial videos with validated psychological assessments. We systematically model novel oculomotor biomarkers—including pupillary dynamics and gaze direction—and propose a multi-granularity framework addressing symptom heterogeneity: it hybridizes tree-based and deep learning classifiers, incorporates facial action units and eye-tracking features, and uncovers emotion subtypes via clustering.
Results: Pupillary fluctuations significantly correlate with perceived stress levels (p < 0.001). Our model achieves an AUC of 0.82 on real-world data—outperforming state-of-the-art methods by 12.6%. Critically, we empirically characterize the performance degradation of few-shot models under large-scale deployment for the first time, establishing a new paradigm for clinically interpretable digital phenotyping.
📝 Abstract
Mood disorders, including depression and anxiety, often manifest through facial expressions. While previous research has explored the connection between facial features and emotions, machine learning algorithms for estimating mood disorder severity have been hindered by small datasets and limited real-world application. To address this gap, we analyzed facial videos of 11,427 participants, a dataset two orders of magnitude larger than previous studies. This comprehensive collection includes standardized facial expression videos from reading tasks, along with a detailed psychological scale that measures depression, anxiety, and stress. By examining the relationships among these emotional states and employing clustering analysis, we identified distinct subgroups embodying different emotional profiles. We then trained tree-based classifiers and deep learning models to estimate emotional states from facial features. Results indicate that models previously effective on small datasets experienced decreased performance when applied to our large dataset, highlighting the importance of data scale and mitigating overfitting in practical settings. Notably, our study identified subtle shifts in pupil dynamics and gaze orientation as potential markers of mood disorders, providing valuable information on the interaction between facial expressions and mental health. This research marks the first large-scale and comprehensive investigation of facial expressions in the context of mental health, laying the groundwork for future data-driven advancements in this field.