Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario

Abstract

Face anti-spoofing is the key to preventing security breaches in biometric recognition applications. Existing software-based and hardwarebased face liveness detection methods are effective in constrained environments or designated datasets only. Deep learning method using RGB and infrared images demands a large amount of training data for new attacks. In this paper, we present a face anti-spoofing method in a realworld scenario by automatic learning the physical characteristics in polarization images of a real face compared to a deceptive attack.