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

I recently went down a rabbit hole after hearing how a major bank’s ATM face scanners were defeated by a literal piece of printer paper. It’s a classic example of what happens when we build for the "happy path" instead of the real world.

If you’re building biometrics for something like employee clock-ins or secure access, you can’t just rely on a basic detection demo-you need a pipeline that actually knows the difference between a human and a photo. This article walks through building a cross-platform, offline-first face recognition system that handles everything from the initial camera frame to the final identity match.

Key Pipeline Features

• Runs lightweight AI models locally via ONNX, making it fast enough for mobile devices and tablets.
• Filters out noise by requiring a minimum detection confidence score of 0.7.
• Cleans up overlapping boxes using Non-Maximum Suppression (NMS) at a 0.4 IoU threshold.
• Implements a dedicated anti-spoofing layer using a 112x112 face crop and a 2-class softmax model.
• Requires a spoof score of 0.1 or lower to ensure the subject is a live person, not a printout or screen.
• Generates 128-dimensional face signatures (embeddings) using FaceNet for high-precision matching.
• Uses HNSW indexing to search through thousands of stored faces in under 1ms.
• Built with .NET MAUI to share the same processing logic across Android, Windows, and cloud environments.

The hard truth is that if your biometric system can be tricked by a $20 desk printer, your entire security model is just theater.

Read the full article here: https://erwinwilsonceniza.qzz.io/blogs/engineering-a-cross-platform-face-recognition-pipeline-with-anti-spoofing