
Facial recognition
Sub-second face verification at NIST FRVT-grade accuracy, with passive liveness detection and privacy built into the architecture.

overview
Deep convolutional and transformer-based recognition supporting 1:1 verification and 1:N identification, with passive liveness blocking photos, replays, masks, and deepfake injections.
The same engine powers digital onboarding, biometric access, attendance, CCTV watchlist matching, and in-app re-auth — one governed system, not a credential per use case.
99%+ true-accept rate at operational false-accept thresholds on NIST FRVT benchmarks. Models tuned to your camera hardware, lighting, and demographic distribution before go-live.
what we build
1:1 verification and 1:N identification at sub-second latency
Passive liveness: detects photos, replays, 3D masks, deepfake injection
Document-to-selfie onboarding with OCR and liveness-checked face comparison
Consent capture, template encryption, configurable retention, audit logging
Adaptive quality gating on resolution, blur, pose, and illumination before matching
how it works
Frame evaluated for resolution, pose, and illumination before processing.
Passive texture, depth, and temporal models detect presentation attacks.
Embedding compared against enrolled template or indexed population.
Accept/reject/escalate record with scores written to audit log.
use cases
Selfie + document OCR + liveness check completes in seconds, no manual review.
Face entry at building or secure zone; credential can't be shared or cloned.
Face challenge before high-risk in-app actions; reduces account takeover.
Biometric credential is inseparable from the person; proxy impossible.
1:N matching alerts on persons of interest or recognizes VIP guests.