Faceoff uses 8 AI models across vision, audio, and physiology for robust trust assessment. Its Trust Factor Engine delivers nuanced trust scores (1–10), unlike binary detectors. Privacy is ensured via stateless, on-premise APIs—no video data is stored or processed in the cloud.

Key Features

Faceoff Technology products come equipped with essential features
designed to safeguard sensitive data and ensure compliance with regulatory standards.

Facial Emotion Recognition
Facial emotion recognition tracks muscle movements and Action Units over time, using heatmaps and micro-expression analysis to uncover subtle, hidden emotions and enhance authenticity detection beyond static snapshots.
Eye-Tracking Emotion Detection
Eye-tracking emotion analysis uses gaze patterns, pupil changes, and blink rates to detect emotional states, revealing anxiety, distraction, or deception through real-time mapping and fixation clusters with high precision.
Posture Analysis
Posture-based analysis leverages joint tracking and motion patterns to interpret emotional states. By analyzing spine alignment, energy shifts, and pose deviations, it reveals openness, tension, or discomfort, enhancing emotional authenticity assessment.
Heart Rate Estimation
Facial signal-based heart rate estimation uses rPPG to monitor BPM changes linked to emotions. RGB shifts and time-synced overlays reveal arousal, stress, or fear, enhanced by motion and lighting correction.
Oxygen Saturation Detection
SpO2 is measured through facial pixel sampling to detect emotions like anger or fear. Time-series plots and light reflection analysis reveal saturation patterns, adding a biometric layer to emotional trust assessment.
Speech Sentiment Analysis
Speech sentiment analysis uses semantic modeling to track tone, word choice, and syntax. It highlights emotional cues and intent, revealing authenticity and aligning spoken language with non-verbal signals for deeper insight.
Audio Tone Sentiment
Audio tone sentiment analysis maps pitch, rhythm, and modulation to emotional cues. Spectrograms and extracted features uncover vocal stress and sincerity, offering deeper insights into emotional intent beyond traditional static tone assessments.
Deepfake Detection
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.