Micro-Expressions by PolygrAI
Turn sub-second facial signals into explainable behavioural evidence.
Introduction
Micro-expressions are involuntary, sub-second facial movements that often reveal a person’s immediate affective response. PolygrAI detects and timestamps these fleeting signals, aligns them with audio and transcript, and presents them as explainable evidence so reviewers can see exactly when felt reaction and spoken claim diverge. This time-aligned view helps teams prioritise review, reduce noise, and make faster, more confident decisions.
What are micro-expressions
Micro-expressions are very brief facial contractions lasting fractions of a second that indicate rapid emotions such as surprise, fear, disgust, contempt or fleeting discomfort. Because they are involuntary and fast, they are difficult for human reviewers to spot reliably; accurate detection requires frame-level computer-vision and temporal modelling.
How we detect them
PolygrAI uses high-frequency video preprocessing, facial landmark tracking and temporal neural models to detect micro-events in context. We learn an individual baseline early in the session, then identify deviations and micro-patterns relative to that baseline. Each detection is timestamped and immediately aligned with the corresponding transcript and audio burst for cross-modal corroboration.
Signals we surface
We convert raw micro-events into reviewer-friendly signals and explainable flags:
• Surprise micro-flares that follow unexpected prompts
• Contempt or dismissive micro-movements (asymmetrical lip/cheek activity)
• Fear-related micro-responses such as eyelid tightening
• Disgust cues like upper-lip or nose wrinkling
• Masking micro-gestures (very brief smiles or rapid adjustments)
• Gaze-aversion events synchronised with micro-facial activity
Why micro-expressions matter
Micro-expressions provide a time-sensitive window into immediate affect. When fused with vocal and linguistic cues they: highlight moments where felt reaction and speech diverge; prioritise statements for human review; lower false positives by requiring cross-modal agreement; and enrich psychometric profiles with fleeting affective indicators absent from text-only data.
Micro-Expressions in AI Interviews Revealing fleeting truth beneath the surface
Micro-expressions are the tiny, involuntary facial twitches that flash across a face in a fraction of a second. In recorded interviews and review sessions these sub-second signals often betray immediate emotional reactions that words conceal. PolygrAI extracts and timestamps those moments during analysis, aligning each micro-event with the exact audio and transcript so reviewers can see where felt reaction and spoken claim diverge. The outcome is precise, explainable evidence that changes the question from “where should I look?” to “what exactly needs review?”
Our detection approach combines frame-accurate visual preprocessing, dense facial-landmark mapping and temporal neural models to surface micro-events against an individual baseline. We learn an interviewee’s natural expressive range early in a session, detect deviations at millisecond resolution, and weight each signal by cross-modal corroboration from voice and language. This baseline-aware method reduces false positives and produces short, human-readable rationales that explain why a moment was elevated and how confident the model is in its assessment.
Micro-expression signals apply across hiring, compliance, underwriting, investigations and claims workflows by turning fleeting affect into operational evidence. Recruiters use timestamped micro-flags to prioritise which candidate answers to watch; compliance teams triage ambiguous KYC responses with concrete visual proof; underwriters and investigators focus manual review on marginal files where behavioural context matters most. All outputs are designed to augment human judgement, with reviewers receiving synchronized video and transcript, concise rationales, suggested follow-ups and exportable evidence bundles that slot directly into existing case files or review queues.
As with all our modalities, micro-expression analysis is privacy first and audit ready. Recordings are processed under explicit consent, storage and retention are configurable, and every flag is linked to immutable timestamps, consent records and encrypted logs for regulatory review. The strongest behavioural insight comes from responsible fusion, combining visual micro-events with vocal, linguistic and physiological signals, and PolygrAI continues to prioritise explainability and defensible outputs as we deepen that multimodal fusion.
OVERVIEW OF OUR TECHNOLOGY
Multi-Modal Analysis Engine
Visual
Our system meticulously analyzes facial micro-expressions, eye movements, gestures, and posture changes alongside subtle body language cues to detect behavioural fluctuations.
Vocal
Our features provide a comprehensive view of the behavioural dynamics in your video session. Each feature explains a key metric and how it helps you make a better, more confident decision.
Linguistic
Leveraging validated psychological metrics, linguistic pattern analysis, and vocal and facial behavior cues, we identify subtle indicators of deception across assessments.
Psychological
Our system applies predictive psychometric modeling, semantic emotion analysis and subtle behavioral cue detection to infer personality drivers and relational dynamics.
Introducing PolygrAI Interviewer
Elevate your hiring process with real-time behavioral insights, seamless video integration, and AI-driven risk scoring for confident candidate decisions.
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Get micro-expression, voice-tone, and sentiment insights as you interview.
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Learns each candidate’s normal behavior patterns to pinpoint subtle deviations under stress.
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Receive post-interview transcripts, risk scores, and emotion summaries for easy review.
Some of the other use cases it can be used within
FAQ
Frequently Asked Questions
Micro-expressions are involuntary, sub-second facial movements that briefly reveal an emotional reaction. They occur faster than conscious control and are useful as momentary affective signals.
We use frame-accurate video preprocessing, dense facial-landmark tracking and temporal neural models to spot tiny muscle contractions, then align each event with the exact audio and transcript for cross-modal context.
Accuracy improves with cross-modal corroboration. Each flag includes a confidence score and a short rationale so reviewers understand why the moment was elevated.
We learn an individual baseline early in the session, require supporting vocal or linguistic cues where possible, and let teams tune sensitivity thresholds to match their operational risk tolerance.
Any modern browser on laptop, tablet or phone works. Optimal results come from a front-facing camera, steady framing, good lighting and minimal background noise. Higher frame rates increase granularity but are not required.
Micro-expression flags are produced during post-processing of submitted recordings and are available in the dashboard shortly after upload. Real-time streaming is a separate enterprise option.
No. Micro-expressions are a signal, not a verdict. They are best used with vocal, linguistic and contextual data to guide human review and decision-making.
Every interview requires explicit consent. Storage, retention and access controls are configurable. Data is encrypted in transit and at rest and audit logs capture chain-of-custody metadata.
Yes. We include liveness checks and deepfake detection layers; suspected manipulation raises additional risk flags and prompts further review.