The Brief
Traditional enterprise facial recognition systems rely on expensive, privacy-invasive cloud APIs (AWS, Azure) that stream sensitive camera feeds to external servers. This creates severe compliance liabilities, recurring infrastructure costs, and vulnerability to network outages or latency bottlenecks. Organizations needed a high-performance, air-gapped biometric solution capable of real-time tracking, 3D mapping, and spoof protection without compromising data sovereignty.
Approach
- 01
Engineered a Python-backed recognition core utilizing ArcFace for 512-D angular embeddings and YuNet for detection, paired with ChromaDB for HNSW vector similarity search and SQLite for relational metadata storage.
- 02
Integrated a dual MiniFASNet ONNX liveness ensemble (2.7x and 4.0x context patches) alongside multi-spectral heuristics and a 3D pose-guided multi-angle capture workflow (CENTER, LEFT, RIGHT, UP, DOWN) to map complete facial geometry.
- 03
Developed an advanced media analysis module supporting scrupulous SAHI tiled scanning for group photos/videos, simultaneous video playback tracking, and a ByteTrack-inspired motion predictor for fast-moving subjects.
Result
Delivered a fully autonomous, offline-first biometric intelligence hub achieving sub-50ms inference latency, robust defense against print and replay spoof attacks, and a modular API-ready architecture ready for enterprise deployment.