  Face presentation attack detection plays a critical role in the modern face
recognition pipeline. A face presentation attack detection model with good
generalization can be obtained when it is trained with face images from
different input distributions and different types of spoof attacks. In reality,
training data (both real face images and spoof images) are not directly shared
between data owners due to legal and privacy issues. In this paper, with the
motivation of circumventing this challenge, we propose a Federated Face
Presentation Attack Detection (FedPAD) framework that simultaneously takes
advantage of rich fPAD information available at different data owners while
preserving data privacy. In the proposed framework, each data center locally
trains its own fPAD model. A server learns a global fPAD model by iteratively
aggregating model updates from all data centers without accessing private data
in each of them. To equip the aggregated fPAD model in the server with better
generalization ability to unseen attacks from users, following the basic idea
of FedPAD, we further propose a Federated Generalized Face Presentation Attack
Detection (FedGPAD) framework. A federated domain disentanglement strategy is
introduced in FedGPAD, which treats each data center as one domain and
decomposes the fPAD model into domain-invariant and domain-specific parts in
each data center. Two parts disentangle the domain-invariant and
domain-specific features from images in each local data center, respectively. A
server learns a global fPAD model by only aggregating domain-invariant parts of
the fPAD models from data centers and thus a more generalized fPAD model can be
aggregated in server. We introduce the experimental setting to evaluate the
proposed FedPAD and FedGPAD frameworks and carry out extensive experiments to
provide various insights about federated learning for fPAD.
