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5 CONCLUSION AND FUTURE
WORK
In this paper, we proposed an approach that exploits
unsupervised adversarial domain adaptation guided
with target clustering, in order to improve the gener-
alization ability for face PAD. Specifically, our frame-
work utilizes UDA to learn domain invariant features
that could leverage from the labeled source samples
to classify the unlabeled samples from target domain.
Yet, the approach succeeds to preserve the intrinsic
properties of the target domain via deep clustering of
target embedding features. Our approach is trained
in an end-to-end fashion and succeeds to reach per-
fect adaptation to the target domain when evaluated
on public benchmark datasets, reaching only 0 - 2%
cross-dataset error. Our future work would focus on
evaluating on more variable datasets, in addition to
reducing the dependency of the model during training
on target domain samples from both classes, trying
to let the model focuses on learning from bona-fide
samples with minimal attack samples contribution.
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