  Face presentation attack detection (PAD) is an essential measure to protect
face recognition systems from being spoofed by malicious users and has
attracted great attention from both academia and industry. Although most of the
existing methods can achieve desired performance to some extent, the
generalization issue of face presentation attack detection under cross-domain
settings (e.g., the setting of unseen attacks and varying illumination) remains
to be solved. In this paper, we propose a novel framework based on asymmetric
modality translation for face presentation attack detection in bi-modality
scenarios. Under the framework, we establish connections between two modality
images of genuine faces. Specifically, a novel modality fusion scheme is
presented that the image of one modality is translated to the other one through
an asymmetric modality translator, then fused with its corresponding paired
image. The fusion result is fed as the input to a discriminator for inference.
The training of the translator is supervised by an asymmetric modality
translation loss. Besides, an illumination normalization module based on
Pattern of Local Gravitational Force (PLGF) representation is used to reduce
the impact of illumination variation. We conduct extensive experiments on three
public datasets, which validate that our method is effective in detecting
various types of attacks and achieves state-of-the-art performance under
different evaluation protocols.
