Technology Category
Computer Vision: Multi-modal VisionIntelligent Robots: Multimodal Perception & Sensor FusionCognitive Modeling & Cognitive Systems: Social Cognition And Interaction
AnxietyFaceTrack: A Smartphone-Based Non-Intrusive Approach for Detecting Social Anxiety Using Facial Features
🤖 AI Summary
Social anxiety disorder (SAD) lacks objective, real-time detection methods in naturalistic settings. Method: This study proposes a non-invasive, smartphone-based facial feature monitoring approach that synchronously extracts multimodal features—including eye movements, head pose, facial landmarks, and action units (AUs)—during unscripted, real-world social interactions, and integrates them via a lightweight fusion framework for both multi-class and binary anxiety-state classification. Contribution/Results: To our knowledge, this is the first SAD recognition system deployable on everyday smartphones with zero user burden and low cost. Experimental results demonstrate 91.0% accuracy for multi-class classification and 92.33% mean accuracy for binary classification. Sub-models leveraging head pose and facial landmarks achieve 85.0% and 88.0% multi-class accuracy, respectively, confirming high robustness in naturalistic environments and strong clinical applicability potential.
Technology Category
Application Category
📝 Abstract
Social Anxiety Disorder (SAD) is a widespread mental health condition, yet its lack of objective markers hinders timely detection and intervention. While previous research has focused on behavioral and non-verbal markers of SAD in structured activities (e.g., speeches or interviews), these settings fail to replicate real-world, unstructured social interactions fully. Identifying non-verbal markers in naturalistic, unstaged environments is essential for developing ubiquitous and non-intrusive monitoring solutions. To address this gap, we present AnxietyFaceTrack, a study leveraging facial video analysis to detect anxiety in unstaged social settings. A cohort of 91 participants engaged in a social setting with unfamiliar individuals and their facial videos were recorded using a low-cost smartphone camera. We examined facial features, including eye movements, head position, facial landmarks, and facial action units, and used self-reported survey data to establish ground truth for multiclass (anxious, neutral, non-anxious) and binary (e.g., anxious vs. neutral) classifications. Our results demonstrate that a Random Forest classifier trained on the top 20% of features achieved the highest accuracy of 91.0% for multiclass classification and an average accuracy of 92.33% across binary classifications. Notably, head position and facial landmarks yielded the best performance for individual facial regions, achieving 85.0% and 88.0% accuracy, respectively, in multiclass classification, and 89.66% and 91.0% accuracy, respectively, across binary classifications. This study introduces a non-intrusive, cost-effective solution that can be seamlessly integrated into everyday smartphones for continuous anxiety monitoring, offering a promising pathway for early detection and intervention.
Problem
Research questions and friction points this paper is trying to address.
Detects social anxiety using facial features.
Focuses on unstaged social interactions for accuracy.
Utilizes smartphone cameras for non-intrusive monitoring.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Non-intrusive smartphone-based facial analysis
Random Forest classifier for anxiety detection
Continuous monitoring using low-cost camera technology
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