# Learning Meta Pattern for Face Anti-Spoofing

arXiv: https://arxiv.org/abs/2110.06753 · HTML: https://mlsanity.com/paper/2110.06753/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 55 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Searching Central Difference Convolutional Networks for Face Anti-Spoofing](https://mlsanity.com/paper/2003.04092/) (2020)
- [Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision](https://mlsanity.com/paper/1803.11097/) (2018)
- [Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1607.03516/) (2016)
- [Single-Side Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2004.14043/) (2020)
- [NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing](https://mlsanity.com/paper/2011.02062/) (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)
- [Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection](https://mlsanity.com/paper/1907.04047/) (2019)
- [Deep Tree Learning for Zero-shot Face Anti-Spoofing](https://mlsanity.com/paper/1904.02860/) (2019)
- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Dual-Cross Central Difference Network for Face Anti-Spoofing](https://mlsanity.com/paper/2105.01290/) (2021)
- [Revisiting Pixel-Wise Supervision for Face Anti-Spoofing](https://mlsanity.com/paper/2011.12032/) (2020)
- [DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing](https://mlsanity.com/paper/2009.07529/) (2020)
- [Camera Invariant Feature Learning for Generalized Face Anti-spoofing](https://mlsanity.com/paper/2101.10075/) (2021)
- [Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing](https://mlsanity.com/paper/1910.03850/) (2019)
- [A Compact Deep Learning Model for Face Spoofing Detection](https://mlsanity.com/paper/2101.04756/) (2021)

## Built on by (most cited first, 29 of 29)

- [Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection](https://mlsanity.com/paper/2304.12489/) (2023)
- [Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues](https://mlsanity.com/paper/2208.05401/) (2022)
- [FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing](https://mlsanity.com/paper/2305.03277/) (2023)
- [Flexible-Modal Face Anti-Spoofing: A Benchmark](https://mlsanity.com/paper/2202.08192/) (2022)
- [Test-Time Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2403.19334/) (2024)
- [Digital and Physical Face Attacks: Reviewing and One Step Further](https://mlsanity.com/paper/2209.14692/) (2022)
- [Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation](https://mlsanity.com/paper/2303.00917/) (2023)
- [Towards Data-Centric Face Anti-Spoofing: Improving Cross-domain Generalization via Physics-based Data Synthesis](https://mlsanity.com/paper/2409.03501/) (2024)
- [One-Class Knowledge Distillation for Face Presentation Attack Detection](https://mlsanity.com/paper/2205.03792/) (2022)
- [Rehearsal-Free Domain Continual Face Anti-Spoofing: Generalize More and Forget Less](https://mlsanity.com/paper/2303.09914/) (2023)
- [S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens](https://mlsanity.com/paper/2309.04038/) (2023)
- [Domain Generalization for Face Anti-spoofing via Content-aware Composite Prompt Engineering](https://mlsanity.com/paper/2504.04470/) (2025)
- [Domain Generalization via Ensemble Stacking for Face Presentation Attack Detection](https://mlsanity.com/paper/2301.02145/) (2023)
- [FaceShield: Explainable Face Anti-Spoofing with Multimodal Large Language Models](https://mlsanity.com/paper/2505.09415/) (2025)
- [M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System](https://mlsanity.com/paper/2301.12831/) (2023)
- [Confidence Aware Learning for Reliable Face Anti-spoofing](https://mlsanity.com/paper/2411.01263/) (2024)
- [Liveness score-based regression neural networks for face anti-spoofing](https://mlsanity.com/paper/2302.09461/) (2023)
- [Domain-Generalized Face Anti-Spoofing with Unknown Attacks](https://mlsanity.com/paper/2310.11758/) (2023)
- [DADM: Dual Alignment of Domain and Modality for Face Anti-spoofing](https://mlsanity.com/paper/2503.00429/) (2025)
- [Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing](https://mlsanity.com/paper/2307.02858/) (2023)
- [FA^{3}-CLIP: Frequency-Aware Cues Fusion and Attack-Agnostic Prompt Learning for Unified Face Attack Detection](https://mlsanity.com/paper/2504.00454/) (2025)
- [Saliency-based Video Summarization for Face Anti-spoofing](https://mlsanity.com/paper/2308.12364/) (2023)
- [Modeling Spoof Noise by De-spoofing Diffusion and its Application in Face Anti-spoofing](https://mlsanity.com/paper/2401.08275/) (2024)
- [A Closer Look at Geometric Temporal Dynamics for Face Anti-Spoofing](https://mlsanity.com/paper/2306.14313/) (2023)
- [Hyperbolic Face Anti-Spoofing](https://mlsanity.com/paper/2308.09107/) (2023)
- [IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images](https://mlsanity.com/paper/2309.17399/) (2023)
- [CPG-PAD: Concept-Informed Prompts Guided Presentation Attack Detection](https://mlsanity.com/paper/2607.01303/) (2026)
- [Semi-Supervised learning for Face Anti-Spoofing using Apex frame](https://mlsanity.com/paper/2309.04958/) (2023)
- [AdvGen: Physical Adversarial Attack on Face Presentation Attack Detection Systems](https://mlsanity.com/paper/2311.11753/) (2023)

## Abstract (arXiv)

Face Anti-Spoofing (FAS) is essential to secure face recognition systems and has been extensively studied in recent years. Although deep neural networks (DNNs) for the FAS task have achieved promising results in intra-dataset experiments with similar distributions of training and testing data, the DNNs' generalization ability is limited under the cross-domain scenarios with different distributions of training and testing data. To improve the generalization ability, recent hybrid methods have been explored to extract task-aware handcrafted features (e.g., Local Binary Pattern) as discriminative information for the input of DNNs. However, the handcrafted feature extraction relies on experts' domain knowledge, and how to choose appropriate handcrafted features is underexplored. To this end, we propose a learnable network to extract Meta Pattern (MP) in our learning-to-learn framework. By replacing handcrafted features with the MP, the discriminative information from MP is capable of learning a more generalized model. Moreover, we devise a two-stream network to hierarchically fuse the input RGB image and the extracted MP by using our proposed Hierarchical Fusion Module (HFM). We conduct comprehensive experiments and show that our MP outperforms the compared handcrafted features. Also, our proposed method with HFM and the MP can achieve state-of-the-art performance on two different domain generalization evaluation benchmarks.