How to Build a Cross-Platform Face Recognition System That Resists Spoofing

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A software engineer has detailed the construction of a cross-platform, offline-first face recognition pipeline designed to prevent spoofing attacks, such as using printed photos to fool biometric systems. The system runs lightweight AI models locally via ONNX, making it suitable for mobile devices and tablets without relying on cloud connectivity. It incorporates a dedicated anti-spoofing layer that analyzes a cropped face image through a two-class model, requiring a spoof score of 0.1 or lower to confirm a live subject. Face embeddings are generated using FaceNet and indexed with HNSW for identity matching in under one millisecond. Built with .NET MAUI, the pipeline shares core processing logic across Android, Windows, and cloud environments, targeting use cases such as employee clock-ins and secure access control.

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