  Face anti-spoofing (FAS) plays a vital role in securing face recognition
systems. Recently, central difference convolution (CDC) has shown its excellent
representation capacity for the FAS task via leveraging local gradient
features. However, aggregating central difference clues from all
neighbors/directions simultaneously makes the CDC redundant and sub-optimized
in the training phase. In this paper, we propose two Cross Central Difference
Convolutions (C-CDC), which exploit the difference of the center and surround
sparse local features from the horizontal/vertical and diagonal directions,
respectively. It is interesting to find that, with only five ninth parameters
and less computational cost, C-CDC even outperforms the full directional CDC.
Based on these two decoupled C-CDC, a powerful Dual-Cross Central Difference
Network (DC-CDN) is established with Cross Feature Interaction Modules (CFIM)
for mutual relation mining and local detailed representation enhancement.
Furthermore, a novel Patch Exchange (PE) augmentation strategy for FAS is
proposed via simply exchanging the face patches as well as their dense labels
from random samples. Thus, the augmented samples contain richer live/spoof
patterns and diverse domain distributions, which benefits the intrinsic and
robust feature learning. Comprehensive experiments are performed on four
benchmark datasets with three testing protocols to demonstrate our
state-of-the-art performance.
