Stress Detection Software:
How Camera-Based FACS Analysis Works

Stress detection software uses physiological signals to identify elevated stress states in individuals. In defence and security contexts, this matters for operator readiness (is this pilot cognitively fit to fly?), credibility assessment (is this person under deception-related stress?), and insider threat monitoring (is this individual showing sustained anomalous stress relative to their baseline?). EchoDepth implements camera-based stress detection using 44 FACS facial Action Units — no wearables, no contact, no specialist hardware.

How Stress Manifests in Facial Action Units

Stress is not a single physiological state — it exists on a spectrum from mild cognitive load to acute fear. FACS research has identified characteristic AU combinations associated with different stress states. Key stress-related AUs include:

• AU1 + AU4 (inner brow raise + brow lowerer): the most reliable involuntary indicator of worry and distress, difficult to consciously suppress
• AU7 (lid tightener): associated with attention and threat appraisal
• AU20 (lip stretcher): acute stress and fear indicator
• AU17 (chin raiser): associated with uncertainty and cognitive conflict
• Elevated arousal in VAD space: the overall activation dimension captures the intensity of the stress state regardless of specific AU combinations

These signals appear in genuine stress states before conscious inhibition can suppress them — and in micro-expression form (1/25 to 1/5 second duration) even when conscious suppression is attempted.

Wearable vs Camera-Based Stress Detection

Wearable stress monitoring devices — ECG patches, galvanic skin response bands, heart rate monitors — provide useful physiological data but require physical attachment to the subject. In defence environments, this creates operational friction, hygiene and device management overhead, and SCIF incompatibility for wireless-transmitting devices.

Camera-based FACS stress detection eliminates these constraints entirely. EchoDepth requires only a standard RGB camera already present in the room. No sensors on the subject. No device management. Fully compatible with SCIF and air-gapped environments. And the FACS signal — drawn from 44 muscle activation channels simultaneously — is arguably richer than single-channel peripheral physiological monitoring.

Real-Time Stress Monitoring: Three Operational Contexts

In operator readiness monitoring, stress detection provides pre-mission readiness scoring and live alerting when cognitive load or stress exceeds safe operational thresholds. A drone pilot showing elevated stress and fatigue AU combinations before a complex mission is a safety and mission-success risk that existing monitoring infrastructure cannot surface.

In credibility assessment, per-question stress mapping identifies which topics produce involuntary stress responses — the temporal pattern of when stress appears relative to specific questions is a structured, reproducible output that withstands legal and procurement scrutiny.

In insider threat monitoring, sustained anomalous stress relative to an individual's established baseline — appearing during routine access events rather than genuinely stressful contexts — is one of the pre-digital signals that precede insider incidents.

Deployment Requirements

EchoDepth's stress detection capability deploys on existing camera infrastructure — no specialist hardware required. Processing is fully on-premise at approximately 700ms latency. The system is SCIF-compatible, air-gap deployable, and integrates with SIEM platforms via REST API and WebSocket for automated alerting workflows.