Faceoff is an advanced AI-based platform that analyzes 30-second video clips using a fusion of eight specialized AI models to determine the authenticity, emotional state, and behavioral intent of a subject.
The objective behind this is to bring Truth, Tech and Trust (3T factor) and the platform produces two core outputs: a Trust Factor and a Final Accuracy Score, which are used across sectors such as law enforcement, insurance, healthcare, customer engagement, social media, and legal proceedings.
Faceoff is not just a static tool; it is a modular, adaptable AI framework designed to address a wide range of real-world challenges through intelligent video analysis. At its core, Faceoff integrates eight foundational AI models. Each AI model outputs a modality-specific confidence score based on its interpretation of the behavioral signal. The ACE engine fuses these scores using weighted logic, taking model accuracy, signal clarity, and inter-modal agreement into account. The result is a normalized Trust Factor (0–5) and a Confidence Level (%) that reflect both credibility and analysis stability.
Faceoff more robust than other tools in the market
| Dimension | Typical AI Systems | Faceoff |
|---|---|---|
| Modality coverage | 1–2 (face, voice) | 8 parallel AIs (eye, face, voice, biometrics, posture, etc.) |
| Signal alignment | Frame-by-frame analysis | Spatiotemporal, frequency & attention-based patterns |
| Real-world robustness | Degrades under noise/occlusion | Recovers via GANs, filters, statistical drift correction |
| Deepfake resilience | Detects limited frame inconsistencies | Detects AV desync, gaze inconsistency, emotion mismatch, heartbeat |
| Explainability | Basic probability | Full signal breakdown with anomaly traceability |
| Decision process | End-to-end black box | ACE fusion engine with explainable trust logic |
What the Trust Factor and Confidence Mean
- Trust Factor (0–10): How human, congruent, and authentic the behavior is
- Confidence (0–1): How certain the system is of the decision
Trust Factor Engine (TFE)
- A proprietary engine that computes a weighted trust score from 8 models, dynamically adjusting weights using ensemble learning and video context.
- → Why it’s better: Unlike binary deepfake detectors (real/fake), Faceoff quantifies human behavior in nuanced trust bands (1–10), which is more practical in real-world assessments.
They are justifiable via:
- Cross-model agreement
- Temporal consistency
- Behavioral entropy vs. known human baselines
- Adversarial robustness (e.g., deepfake resistance)