Face Presentation Attack Detection by Excavating Causal Clues and
  Adapting Embedding Statistics
Meiling Fang Naser Damer
Recent face presentation attack detection (PAD) leverages domain adaptation
(DA) and domain generalization (DG) techniques to address performance
degradation on unknown domains. However, DA-based PAD methods require access to
unlabeled target data, while most DG-based PAD solutions rely on a priori,
i.e., known domain labels. Moreover, most DA-/DG-based methods are
computationally intensive, demanding complex model architectures and/or
multi-stage training processes. This paper proposes to model face PAD as a
compound DG task from a causal perspective, linking it to model optimization.
We excavate the causal factors hidden in the high-level representation via
counterfactual intervention. Moreover, we introduce a class-guided MixStyle to
enrich feature-level data distribution within classes instead of focusing on
domain information. Both class-guided MixStyle and counterfactual intervention
components introduce no extra trainable parameters and negligible computational
resources. Extensive cross-dataset and analytic experiments demonstrate the
effectiveness and efficiency of our method compared to state-of-the-art PADs.
The implementation and the trained weights are publicly available.