Fang, Meiling ; Damer, Naser (2024)
Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statistics.
Winter Conference on Applications of Computer Vision 2024. Waikoloa, USA (04.01.-08.01.2024)
doi: 10.1109/WACV57701.2024.00615
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Fang, Meiling ; Damer, Naser |
| Art des Eintrags: | Bibliographie |
| Titel: | Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statistics |
| Sprache: | Englisch |
| Publikationsjahr: | 9 April 2024 |
| Verlag: | IEEE |
| Buchtitel: | Proceedings: 2024 IEEE/CVF Winter Conference on Applications of Computer Vision |
| Veranstaltungstitel: | Winter Conference on Applications of Computer Vision 2024 |
| Veranstaltungsort: | Waikoloa, USA |
| Veranstaltungsdatum: | 04.01.-08.01.2024 |
| DOI: | 10.1109/WACV57701.2024.00615 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Biometrics, Face recognition, Spoofing attacks, Machine learning, Deep learning |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 26 Apr 2024 19:20 |
| Letzte Änderung: | 26 Apr 2024 19:20 |
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