How Liveness Detection Stops Photos and Videos From Defeating Face Recognition

Liveness detection is a security layer added to face recognition systems to verify that a real, live person is present rather than a photo or video replay. It works through two approaches: passive checks, which analyze frames for spoofing signs like paper texture or screen moiré, and active challenges, which prompt users to blink or turn their head. Combining a passive check on every frame with a randomized active challenge at enrollment offers the strongest defense against common attacks, including printed photos, phone screens, and pre-recorded videos. Browser-based implementations can run a lightweight anti-spoofing model locally using ONNX, keeping facial data on the device and avoiding continuous server streaming. Developers are advised to test with real spoof materials across multiple devices and to monitor both spoof acceptance and live rejection rates to ensure the system remains both secure and usable.