Fang, MeilingMeilingFangDamer, NaserNaserDamer2024-04-172024-04-242024-04-172024https://publica.fraunhofer.de/handle/publica/46613310.1109/WACV57701.2024.00615Recent 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.enBranche: Information TechnologyResearch Line: Computer vision (CV)Research Line: Human computer interaction (HCI)Research Line: Machine learning (ML)LTA: Interactive decision-making support and assistance systemsLTA: Machine intelligence, algorithms, and data structures (incl. semantics)LTA: Generation, capture, processing, and output of images and 3D modelsBiometricsFace recognitionSpoofing attacksMachine learningDeep learningATHENEconference paper