Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statistics

• URL: http://arxiv.org/abs/2308.14551v1
• Date: Mon, 28 Aug 2023 13:11:05 GMT
• Title: Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statistics
• Authors: Meiling Fang and Naser Damer
• Abstract summary: Face presentation attack detection (PAD) uses domain adaptation (DA) and domain generalization (DG) techniques to address performance degradation on unknown domains. Most DG-based PAD solutions rely on a priori, i.e., known domain labels. This paper proposes to model face PAD as a compound DG task from a causal perspective, linking it to model optimization.
• Score: 9.612556145185431
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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