Engineering Progress
Where the system has actually been built and tested so far — no live customer installations yet.
Every installation starts with a working core system — detection, matching, and alerting validated end-to-end in controlled testing — before any live rollout. The list below reflects where that process actually stands today, not a roadmap.
Ongoing
- No ongoing installations listed yet.
Completed
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Completed
Phase 1 — Core Detection & Matching Pipeline
GPU-accelerated face detection and matching pipeline, tested end-to-end with true-positive and true-negative verification against enrolled faces.
Engineering facts
Case Study
Phase 1 — Core Detection & Matching Pipeline
Build a face-recognition access-monitoring pipeline fast enough for live video, accurate enough for real-world lighting and angles, and honest about its own confidence rather than a blunt match/no-match.
CNN-based detection instead of the faster-but-weaker HOG detector, CLAHE lighting preprocessing before every detection pass, GPU acceleration via CUDA/cuDNN, and a three-tier confidence system (authorized / borderline / unknown) instead of a binary decision.
GPU-accelerated detection measured at ~0.12s/frame versus ~40.6s/frame CPU-only (~340x faster) on an RTX 4060 — verified end-to-end with true-positive and true-negative testing against enrolled faces.