# Style-Guided Domain Adaptation for Face Presentation Attack Detection

arXiv: https://arxiv.org/abs/2203.14565 · HTML: https://mlsanity.com/paper/2203.14565/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening](https://mlsanity.com/paper/2103.15597/) (2021)
- [Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network](https://mlsanity.com/paper/1803.07835/) (2018)
- [Domain Generalization Using a Mixture of Multiple Latent Domains](https://mlsanity.com/paper/1911.07661/) (2019)
- [Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision](https://mlsanity.com/paper/1803.11097/) (2018)
- [Demystifying Neural Style Transfer](https://mlsanity.com/paper/1701.01036/) (2017)
- [Single-Side Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2004.14043/) (2020)
- [Regularized Fine-grained Meta Face Anti-spoofing](https://mlsanity.com/paper/1911.10771/) (2019)
- [Learn Convolutional Neural Network for Face Anti-Spoofing](https://mlsanity.com/paper/1408.5601/) (2014)
- [Self-Domain Adaptation for Face Anti-Spoofing](https://mlsanity.com/paper/2102.12129/) (2021)
- [Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2110.04111/) (2021)

## Built on by (most cited first, 1 of 1)

- [Test-Time Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2403.19334/) (2024)

## Abstract (arXiv)

Domain adaptation (DA) or domain generalization (DG) for face presentation attack detection (PAD) has attracted attention recently with its robustness against unseen attack scenarios. Existing DA/DG-based PAD methods, however, have not yet fully explored the domain-specific style information that can provide knowledge regarding attack styles (e.g., materials, background, illumination and resolution). In this paper, we introduce a novel Style-Guided Domain Adaptation (SGDA) framework for inference-time adaptive PAD. Specifically, Style-Selective Normalization (SSN) is proposed to explore the domain-specific style information within the high-order feature statistics. The proposed SSN enables the adaptation of the model to the target domain by reducing the style difference between the target and the source domains. Moreover, we carefully design Style-Aware Meta-Learning (SAML) to boost the adaptation ability, which simulates the inference-time adaptation with style selection process on virtual test domain. In contrast to previous domain adaptation approaches, our method does not require either additional auxiliary models (e.g., domain adaptors) or the unlabeled target domain during training, which makes our method more practical to PAD task. To verify our experiments, we utilize the public datasets: MSU-MFSD, CASIA-FASD, OULU-NPU and Idiap REPLAYATTACK. In most assessments, the result demonstrates a notable gap of performance compared to the conventional DA/DG-based PAD methods.