# Domain Generalization with Pseudo-Domain Label for Face Anti-Spoofing

arXiv: https://arxiv.org/abs/2107.06552 · HTML: https://mlsanity.com/paper/2107.06552/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 53 references that are in the map.
- First posted on arXiv 14 Jul 2021.

## Builds on (most cited first, 26 of 26)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [Unified Deep Supervised Domain Adaptation and Generalization](https://mlsanity.com/paper/1709.10190/) (2017)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network](https://mlsanity.com/paper/1803.07835/) (2018)
- [Searching Central Difference Convolutional Networks for Face Anti-Spoofing](https://mlsanity.com/paper/2003.04092/) (2020)
- [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)
- [Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering](https://mlsanity.com/paper/2003.08607/) (2020)
- [Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations](https://mlsanity.com/paper/2003.12237/) (2020)
- [Meta-Learning without Memorization](https://mlsanity.com/paper/1912.03820/) (2019)
- [Regularized Fine-grained Meta Face Anti-spoofing](https://mlsanity.com/paper/1911.10771/) (2019)
- [AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations](https://mlsanity.com/paper/1905.00292/) (2019)
- [Unsupervised Learning via Meta-Learning](https://mlsanity.com/paper/1810.02334/) (2018)
- [Learn Convolutional Neural Network for Face Anti-Spoofing](https://mlsanity.com/paper/1408.5601/) (2014)
- [Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection](https://mlsanity.com/paper/1907.04047/) (2019)
- [Face Anti-Spoofing Via Disentangled Representation Learning](https://mlsanity.com/paper/2008.08250/) (2020)
- [Face Anti-Spoofing with Human Material Perception](https://mlsanity.com/paper/2007.02157/) (2020)
- [Face De-Spoofing: Anti-Spoofing via Noise Modeling](https://mlsanity.com/paper/1807.09968/) (2018)
- [Self-Domain Adaptation for Face Anti-Spoofing](https://mlsanity.com/paper/2102.12129/) (2021)
- [Customizable Architecture Search for Semantic Segmentation](https://mlsanity.com/paper/1908.09550/) (2019)
- [Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation](https://mlsanity.com/paper/2106.13292/) (2021)

## Built on by (most cited first, 2 of 2)

- [Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing](https://mlsanity.com/paper/2112.14894/) (2021)
- [Deep Learning meets Liveness Detection: Recent Advancements and Challenges](https://mlsanity.com/paper/2112.14796/) (2021)

## Abstract (arXiv)

Face anti-spoofing (FAS) plays an important role in protecting face recognition systems from face representation attacks. Many recent studies in FAS have approached this problem with domain generalization technique. Domain generalization aims to increase generalization performance to better detect various types of attacks and unseen attacks. However, previous studies in this area have defined each domain simply as an anti-spoofing datasets and focused on developing learning techniques. In this paper, we proposed a method that enables network to judge its domain by itself with the clustered convolutional feature statistics from intermediate layers of the network, without labeling domains as datasets. We obtained pseudo-domain labels by not only using the network extracting features, but also using depth estimators, which were previously used only as an auxiliary task in FAS. In our experiments, we trained with three datasets and evaluated the performance with the remaining one dataset to demonstrate the effectiveness of the proposed method by conducting a total of four sets of experiments.