VoiceMatch AI

Faceoff’s deepfake detection combines facial analysis, motion tracking, and texture anomalies. By assessing blinking, symmetry, and lighting with temporal coherence, it uncovers synthetic edits and ensures video authenticity and trust.

Multi-AI Fusion Architecture

Faceoff is not reliant on a single modality; it fuses 8 AI models working in parallel (vision, audio, physiological signal estimation) for a more holistic trust and authenticity assessment.
→ Why it’s better: Traditional systems mostly use only facial recognition or emotion detection alone. Faceoff combines cross-domain cues for robustness.

Privacy-Preserving API Deployment

Faceoff does not ask for or store video data. It only provides stateless APIs for enterprises to use on their private cloud or infrastructure. Only metadata like number of API calls is tracked.
→ Why it’s better: Most SaaS tools process videos in vendor’s cloud, risking data breaches. Faceoff's architecture supports on-premise privacy.

How Each of the 8 AI Models Work

Model Name	Input	What It Detects
Facial Emotion Recognition	Video frames	Human emotion state (anger, joy, etc.)
Eye-Tracking Emotion Detection	Eye region	Stress, deception cues, fatigue
Posture Analysis	Full body from video	Nervous gestures, alertness, assertiveness
Heart Rate Estimation	Face color changes	Real-time BPM
Oxygen Saturation Detection	Facial RGB video	Blood oxygen (SpO₂) estimate
Speech Sentiment Analysis	Voice waveform	Emotion from spoken content
Audio Tone Sentiment	Audio pitch + tone	Tone-based intent (anger, sarcasm)
Deepfake Detection	Frame consistency	Video authenticity probability

30-Second Rapid Video Analysis

All models are trained and optimized for short clips (15–30 seconds), making them suitable for social media, HR screening, fraud detection, and forensic analysis.
→ Why it’s better: Other systems need longer footage or higher resolution to work reliably.

Physiological Signal Estimation (SpO₂, HR) from Face Only

Extracting remote PPG and oxygen saturation using camera input — no wearables or special sensors.
→ Why it’s better: Industry tools don’t integrate bio-signals into authenticity assessment, making Faceoff uniquely biomimetic.

Behavioral and Sentiment Correlation Model

Maps emotion, posture, voice tone, and speech semantics to validate behavioral consistency.
→ Why it’s better: Deepfakes may mimic expressions but struggle to maintain alignment across modalities.

Real-Time AI Decision Pipeline

Faceoff offers real-time feedback (2–3 seconds) on video input.
→ Why it’s better: Traditional forensic or ML pipelines may take minutes or hours for analysis.