The original title is "Engineering a Cross-Platform Face Recognition Pipeline with Anti-Spoofing"

Short summary

This technical guide walks through building a production-ready cross-platform face recognition system with anti-spoofing defenses using ONNX, FaceNet embeddings, and HNSW indexing. The article emphasizes why basic face detection alone fails—genuine anti-spoofing mechanisms are critical to prevent attacks like defeating ATM scanners with printed photos. Covers detection confidence thresholds (0.7), Non-Maximum Suppression, live-person verification (spoof score <0.1), and sub-millisecond matching across thousands of faces.

• •Production face recognition pipeline with anti-spoofing layer prevents attacks like printer-paper ATM defeats
• •Technical stack: ONNX models, FaceNet 128-D embeddings, HNSW indexing for <1ms matching, .NET MAUI for cross-platform
• •Specific thresholds and algorithms provided: 0.7 confidence, 0.4 IoU NMS, 112x112 crop, spoof score ≤0.1 for live detection

Generated with AI, which can make mistakes.