  This work studies the generalization issue of face anti-spoofing (FAS) models
on domain gaps, such as image resolution, blurriness and sensor variations.
Most prior works regard domain-specific signals as a negative impact, and apply
metric learning or adversarial losses to remove them from feature
representation. Though learning a domain-invariant feature space is viable for
the training data, we show that the feature shift still exists in an unseen
test domain, which backfires on the generalizability of the classifier. In this
work, instead of constructing a domain-invariant feature space, we encourage
domain separability while aligning the live-to-spoof transition (i.e., the
trajectory from live to spoof) to be the same for all domains. We formulate
this FAS strategy of separability and alignment (SA-FAS) as a problem of
invariant risk minimization (IRM), and learn domain-variant feature
representation but domain-invariant classifier. We demonstrate the
effectiveness of SA-FAS on challenging cross-domain FAS datasets and establish
state-of-the-art performance.
