From 12% to 80% Accuracy — A Broken System Made Production-Ready
Took a failing facial recognition deployment from unusable to operational in federal security environments — by solving the problem the original system couldn't see.
Client Type
Federal Agency
Timeline
TBC
Role
Lead AI Engineer
A federal security deployment couldn't match faces from live camera feeds to passport photos — and it had to run offline.
The client had deployed a facial recognition system across federal buildings to identify individuals against a passport photo database. The system was failing at 12% accuracy — effectively useless.
Two problems compounded each other: CCTV cameras captured faces at oblique angles and low resolution, while the reference database contained only front-facing passport photos. The angle mismatch made direct comparison nearly impossible. On top of that, the system had to run entirely offline on standard GPU hardware across 20+ simultaneous camera feeds — no cloud, no external APIs, no room for slow inference.
12% → 80%
Recognition accuracy improvement
24 FPS
Near real-time on 20+ simultaneous cameras
Offline
Fully air-gapped, no cloud dependency
High-Performance Detection Pipeline
Built a complete facial recognition pipeline from scratch using ArcFace — detection, cropping, normalization, and recognition. Optimized with FAISS-indexed cosine similarity to achieve near real-time processing at 24 frames per second across 20+ concurrent camera streams on standard GPU hardware.
GAN-Based Face Synthesis
This was the breakthrough. Developed a GAN model that could take oblique, low-quality CCTV captures and synthesize a front-facing passport-style representation. This solved the fundamental angle mismatch that was destroying the original system’s accuracy.
Hardware Optimization
Developed custom CUDA kernels and optimized the pipeline for TensorRT-enabled hardware. The entire system ran offline on air-gapped infrastructure — no cloud calls, no external dependencies, meeting strict federal security requirements.
End-to-End Integration
Delivered a production system that could ingest live feeds from 20+ cameras, run face detection and GAN synthesis in real-time, match against the passport database, and surface results to operators — all within federal security and privacy constraints.
BEFORE
- 12% facial recognition accuracy — functionally useless
- CCTV angle mismatch against passport photos unsolved
- System couldn’t keep up with multi-camera feeds in real-time
- No viable offline solution for air-gapped environments
AFTER
- 80% recognition accuracy — a 6.7x improvement
- GAN synthesis bridged the CCTV-to-passport angle gap for the first time
- Near real-time processing at 24 FPS across 20+ concurrent cameras
- Fully offline, air-gapped deployment meeting federal security requirements
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