mlsanityData to 13 Oct 2026

Domain Generalization via Ensemble Stacking for Face Presentation Attack Detection

arXiv 2301.02145 ↗First posted 5 Jan 2023cs.CVcited by 6

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Abstract (arXiv)

Face Presentation Attack Detection (PAD) plays a pivotal role in securing face recognition systems against spoofing attacks. Although great progress has been made in designing face PAD methods, developing a model that can generalize well to unseen test domains remains a significant challenge. Moreover, due to different types of spoofing attacks, creating a dataset with a sufficient number of samples for training deep neural networks is a laborious task. This work proposes a comprehensive solution that combines synthetic data generation and deep ensemble learning to enhance the generalization capabilities of face PAD. Specifically, synthetic data is generated by blending a static image with spatiotemporal encoded images using alpha composition and video distillation. This way, we simulate motion blur with varying alpha values, thereby generating diverse subsets of synthetic data that contribute to a more enriched training set. Furthermore, multiple base models are trained on each subset of synthetic data using stacked ensemble learning. This allows the models to learn complementary features and representations from different synthetic subsets. The meta-features generated by the base models are used as input to a new model called the meta-model. The latter combines the predictions from the base models, leveraging their complementary information to better handle unseen target domains and enhance the overall performance. Experimental results on four datasets demonstrate low half total error rates (HTERs) on three benchmark datasets: CASIA-MFSD (8.92%), MSU-MFSD (4.81%), and OULU-NPU (6.70%). The approach shows potential for advancing presentation attack detection by utilizing large-scale synthetic data and the meta-model.

Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). Builds on 43 of its 105 references that are in the map. First posted on arXiv 5 Jan 2023.

Builds on

43 of 105 references.

References · 43

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31 more
  1. 2021Ensemble deep learning: A reviewcited by 284
  2. 2018Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervisioncited by 167
  3. 2020Domain Adaptive Ensemble Learningcited by 156
  4. 2020Single-Side Domain Generalization for Face Anti-Spoofingcited by 139
  5. 2020Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challengescited by 120
  6. 2020NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofingcited by 105
  7. 2020Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learningcited by 93
  8. 2019Regularized Fine-grained Meta Face Anti-spoofingcited by 86
  9. 2019Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Networkcited by 78
  10. 2019Deep Pixel-wise Binary Supervision for Face Presentation Attack Detectioncited by 76
  11. 2019Deep Tree Learning for Zero-shot Face Anti-Spoofingcited by 73
  12. 2022Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofingcited by 73
  13. 2020Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofingcited by 70
  14. 2019CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofingcited by 61
  15. 2018A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofingcited by 51
  16. 2022Multi-domain Learning for Updating Face Anti-spoofing Modelscited by 39
  17. 2022Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofingcited by 38
  18. 2020Multi-Modal Face Anti-Spoofing Based on Central Difference Networkscited by 36
  19. 2021Meta-Teacher For Face Anti-Spoofingcited by 33
  20. 2022Generative Domain Adaptation for Face Anti-Spoofingcited by 33
  21. 2021Learning Meta Pattern for Face Anti-Spoofingcited by 29
  22. 2021Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofingcited by 23
  23. 20203D Face Anti-spoofing with Factorized Bilinear Codingcited by 20
  24. 2021Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learningcited by 20
  25. 2019Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofingcited by 19
  26. 2019Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problemscited by 15
  27. 2021Consistency Regularization for Deep Face Anti-Spoofingcited by 10
  28. 2022Cyclically Disentangled Feature Translation for Face Anti-spoofingcited by 10
  29. 2022Self-Supervised Face Presentation Attack Detection with Dynamic Grayscale Snippetscited by 5
  30. 2022Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Losscited by 5
  31. 2022Face Anti-Spoofing from the Perspective of Data Samplingcited by 4

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 1; 2025: 2.

6 papers.

  1. 2023Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofingcited by 3
  2. 2023Saliency-based Video Summarization for Face Anti-spoofingcited by 2
  3. 2024Meta Curvature-Aware Minimization for Domain Generalizationcited by 1
  4. 2025Learning Unknown Spoof Prompts for Generalized Face Anti-Spoofing Using Only Real Face Imagescited by 1
  5. 2025Variational Learning of Disentangled Representationscited by 1
  6. 2023Semi-Supervised learning for Face Anti-Spoofing using Apex framecited by 0