Engineering a Cross-Platform Face Recognition Pipeline with Anti-Spoofing

A developer built a production-grade cross-platform face recognition pipeline with anti-spoofing, using a lightweight RFB-320 face detector, FaceNet-style embeddings, and HNSW indexing for sub-millisecond matching. The system runs offline on Android tablets with RSA-licensed deployment and includes dynamic threshold adjustment that reduces false accepts by ~30%.

Check out my article on this blog spot, it talks about building a face recognition system that actually works in production — not a demo, not a toy, something you can put on an Android tablet mounted on a warehouse wall and walk away. It covers the full pipeline: - Finding the face with a lightweight RFB-320 model 1.27 MB, runs on CPU - Anti-spoofing to stop print and replay attacks 0.1 threshold, 13.9 MB model - FaceNet-style 128-dim embeddings with L2 normalization - HNSW indexing for sub-millisecond matching at 10,000+ enrollees - Dynamic gap-based threshold adjustment that cuts false accepts by ~30% - Thread-safe ONNX inference with three models running sequentially - Offline RSA-licensed deployment for factories, mines, and remote sites - Real issues we hit: channel order bugs, semaphore starvation, per-device liveness drift, cold start latency Read the full article here: 👉 Engineering a Cross-Platform Face Recognition Pipeline with Anti-Spoofing https://erwinwilsonceniza.qzz.io/blogs/engineering-a-cross-platform-face-recognition-pipeline-with-anti-spoofing