FAS-UCM generates user-specific spoof images via style transfer to train a CNN that distinguishes live and spoof faces with 0.22 average error rate on the SiW database.
OULU- NPU: A mobile face presentation attack database with real-world variations,
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Style Transfer Applied to Face Liveness Detection with User-Centered Models
FAS-UCM generates user-specific spoof images via style transfer to train a CNN that distinguishes live and spoof faces with 0.22 average error rate on the SiW database.