Research map / Label-efficient and robust learning / Recognition and long-tail
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace binary classification supervision with depth-map supervision (ablation) | CNN face anti-spoofing model · Oulu Protocol 1 cross-testing, FDR 1% | TDR: 8.5\% -> 40.2\% | Helped |
| Add rPPG-supervised RNN to the depth-supervised CNN (ablation) | CNN face anti-spoofing model · Oulu Protocol 1 cross-testing, FDR 1% | TDR: 40.2\% -> 45.8\% | Helped |
| Add the non-rigid registration layer to the CNN-RNN model (ablation) | CNN-RNN face anti-spoofing model · Oulu Protocol 1 cross-testing, FDR 1% | TDR: 39.4\% -> 45.8\% | Helped |
| Increase sequence length for rPPG estimation (ablation) | Proposed method · Oulu Protocol 2 | ACER: 4.16\% (train/test 5) -> 3.61\% (train/test 10) -> 2.98\% (train/test 20) | Helped |
| Use the proposed auxiliary-supervision model instead of GRADIANT | Face anti-spoofing on Oulu Protocol 1 · Oulu Protocol 1 | ACER: 6.9 -> 1.6 | Helped |
| Use the proposed auxiliary-supervision model instead of GRADIANT | Face anti-spoofing on Oulu Protocol 2 · Oulu Protocol 2 | ACER: 2.5 -> 2.7 | Hurt |
| Use the proposed auxiliary-supervision model instead of Colour Texture | Cross-database face anti-spoofing · CASIA-MFSD train, Replay-Attack test | HTER: 30.3% -> 27.6% | Helped |
| Use the proposed auxiliary-supervision model instead of Colour Texture | Cross-database face anti-spoofing · Replay-Attack train, CASIA-MFSD test | HTER: 37.7% -> 28.4% | Helped |
Closest papers by meaning
- FaceShield: Explainable Face Anti-Spoofing with Multimodal Large Language Models (AAAI 2026 · similarity 0.90)
- SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining (AAAI 2025 · similarity 0.88)
- Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models (AAAI 2025 · similarity 0.88)
- Test-Time Domain Generalization for Face Anti-Spoofing (CVPR 2024 · similarity 0.88)
- One-Class Face Anti-spoofing via Spoof Cue Map-Guided Feature Learning (CVPR 2024 · similarity 0.88)
- From Intuition to Investigation: A Tool-Augmented Reasoning MLLM Framework for Generalizable Face Anti-Spoofing (CVPR 2026 · similarity 0.88)
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