Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing

Understand

Face anti-spoofing (a.k.a presentation attack detection) has drawn growing attention due to the high-security demand in face authentication systems.

• Existing CNN-based approaches usually well recognize the spoofing faces when training and testing spoofing samples display similar patterns, but their performance would drop drastically on testing spoofing faces of unseen scenes.
• In this paper, we try to boost the generalizability and applicability of these methods by designing a CNN model with two major novelties.
• First, we propose a simple yet effective Total Pairwise Confusion (TPC) loss for CNN training, which enhances the generalizability of the learned Presentation Attack (PA) representations.