# Adversarial Unsupervised Domain Adaptation Guided with Deep Clustering for Face Presentation Attack Detection

arXiv: https://arxiv.org/abs/2102.06864 · HTML: https://mlsanity.com/paper/2102.06864/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 13 Feb 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Multi-Adversarial Domain Adaptation](https://mlsanity.com/paper/1809.02176/) (2018)
- [Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering](https://mlsanity.com/paper/2003.08607/) (2020)
- [Single-Side Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2004.14043/) (2020)
- [Domain-Symmetric Networks for Adversarial Domain Adaptation](https://mlsanity.com/paper/1904.04663/) (2019)
- [Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization](https://mlsanity.com/paper/1704.06327/) (2017)
- [Adversarial Dropout Regularization](https://mlsanity.com/paper/1711.01575/) (2017)
- [Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning](https://mlsanity.com/paper/2004.01959/) (2020)
- [Discriminative Adversarial Domain Adaptation](https://mlsanity.com/paper/1911.12036/) (2019)
- [Looking back at Labels: A Class based Domain Adaptation Technique](https://mlsanity.com/paper/1904.01341/) (2019)
- [A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing](https://mlsanity.com/paper/1805.04176/) (2018)
- [Exploiting temporal and depth information for multi-frame face anti-spoofing](https://mlsanity.com/paper/1811.05118/) (2018)
- [Deep convolutional neural networks for face and iris presentation attack detection: Survey and case study](https://mlsanity.com/paper/2004.12040/) (2020)
- [Discriminative Clustering for Robust Unsupervised Domain Adaptation](https://mlsanity.com/paper/1905.13331/) (2019)

## Abstract (arXiv)

Face Presentation Attack Detection (PAD) has drawn increasing attentions to secure the face recognition systems that are widely used in many applications. Conventional face anti-spoofing methods have been proposed, assuming that testing is from the same domain used for training, and so cannot generalize well on unseen attack scenarios. The trained models tend to overfit to the acquisition sensors and attack types available in the training data. In light of this, we propose an end-to-end learning framework based on Domain Adaptation (DA) to improve PAD generalization capability. Labeled source-domain samples are used to train the feature extractor and classifier via cross-entropy loss, while unsupervised data from the target domain are utilized in adversarial DA approach causing the model to learn domain-invariant features. Using DA alone in face PAD fails to adapt well to target domain that is acquired in different conditions with different devices and attack types than the source domain. And so, in order to keep the intrinsic properties of the target domain, deep clustering of target samples is performed. Training and deep clustering are performed end-to-end, and experiments performed on several public benchmark datasets validate that our proposed Deep Clustering guided Unsupervised Domain Adaptation (DCDA) can learn more generalized information compared with the state-of-the-art classification error on the target domain.