mlsanityData to 7 Oct 2026

Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss

arXiv 2211.15955 ↗First posted 29 Nov 2022cs.CVcited by 5

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Abstract (arXiv)

With the increasing variations of face presentation attacks, model generalization becomes an essential challenge for a practical face anti-spoofing system. This paper presents a generalized face anti-spoofing framework that consists of three tasks: depth estimation, face parsing, and live/spoof classification. With the pixel-wise supervision from the face parsing and depth estimation tasks, the regularized features can better distinguish spoof faces. While simulating domain shift with meta-learning techniques, the proposed one-side triplet loss can further improve the generalization capability by a large margin. Extensive experiments on four public datasets demonstrate that the proposed framework and training strategies are more effective than previous works for model generalization to unseen domains.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 19 of its 29 references that are in the map. First posted on arXiv 29 Nov 2022.

Builds on

19 of 29 references.

References · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  3. 2018BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentationcited by 645
  4. 2019ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networkscited by 572
  5. 2018Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Networkcited by 195
  6. 2018Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervisioncited by 167
  7. 2020Single-Side Domain Generalization for Face Anti-Spoofingcited by 139
  8. 2020Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learningcited by 93
  9. 2019Regularized Fine-grained Meta Face Anti-spoofingcited by 86
  10. 2014Learn Convolutional Neural Network for Face Anti-Spoofingcited by 80
  11. 2022Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofingcited by 73
  12. 2022PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognitioncited by 67
7 more
  1. 2021Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofingcited by 49
  2. 2021Generalizable Representation Learning for Mixture Domain Face Anti-Spoofingcited by 46
  3. 2021Meta-Teacher For Face Anti-Spoofingcited by 33
  4. 2021Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofingcited by 23
  5. 2019Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofingcited by 19
  6. 2017Improving Facial Attribute Prediction using Semantic Segmentationcited by 16
  7. 2021Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detectioncited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2023Domain Generalization via Ensemble Stacking for Face Presentation Attack Detectioncited by 6
  2. 2023Saliency-based Video Summarization for Face Anti-spoofingcited by 2
  3. 2024Principles of Designing Robust Remote Face Anti-Spoofing Systemscited by 2
  4. 2025Virtual camera detection: Catching video injection attacks in remote biometric systemscited by 0
  5. 2026Domain-generalizable Face Anti-Spoofing with Patch-based Multi-tasking and Artifact Pattern Conversioncited by 0