G2V2former: Graph Guided Video Vision Transformer for Face Anti-Spoofing
Not on the map. Why?
Abstract (arXiv)
In videos containing spoofed faces, we may uncover the spoofing evidence based on either photometric or dynamic abnormality, even a combination of both. Prevailing face anti-spoofing (FAS) approaches generally concentrate on the single-frame scenario, however, purely photometric-driven methods overlook the dynamic spoofing clues that may be exposed over time. This may lead FAS systems to conclude incorrect judgments, especially in cases where it is easily distinguishable in terms of dynamics but challenging to discern in terms of photometrics. To this end, we propose the Graph Guided Video Vision Transformer (G2V2former), which combines faces with facial landmarks for photometric and dynamic feature fusion. We factorize the attention into space and time, and fuse them via a spatiotemporal block. Specifically, we design a novel temporal attention called Kronecker temporal attention, which has a wider receptive field, and is beneficial for capturing dynamic information. Moreover, we leverage the low-semantic motion of facial landmarks to guide the high-semantic change of facial expressions based on the motivation that regions containing landmarks may reveal more dynamic clues. Extensive experiments on nine benchmark datasets demonstrate that our method achieves superior performance under various scenarios. The codes will be released soon.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 49 of its 84 references that are in the map. First posted on arXiv 14 Aug 2024.
Builds on
49 of 84 references.
References · 49
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
- 2021ViViT: A Video Vision Transformercited by 1,610
- 2021Is Space-Time Attention All You Need for Video Understanding?cited by 1,609
- 2022VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Trainingcited by 1,305
- 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
- 2021Video Swin Transformercited by 1,090
37 more
- 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 895
- 2020Transformer Interpretability Beyond Attention Visualizationcited by 620
- 2022Masked Autoencoders As Spatiotemporal Learnerscited by 428
- 2021Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognitioncited by 317
- 2021Video Transformer Networkcited by 271
- 2020Towards Fast, Accurate and Stable 3D Dense Face Alignmentcited by 219
- 2020Searching Central Difference Convolutional Networks for Face Anti-Spoofingcited by 173
- 2018Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervisioncited by 167
- 2020Single-Side Domain Generalization for Face Anti-Spoofingcited by 139
- 2020NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofingcited by 104
- 2019Regularized Fine-grained Meta Face Anti-spoofingcited by 86
- 2015face anti-spoofing based on color texture analysiscited by 82
- 2019Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Networkcited by 78
- 2022Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofingcited by 73
- 2020Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofingcited by 70
- 2022PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognitioncited by 67
- 2020CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofingcited by 66
- 2020On Disentangling Spoof Trace for Generic Face Anti-Spoofingcited by 62
- 2019CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofingcited by 61
- 2018A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofingcited by 51
- 2021Self-Domain Adaptation for Face Anti-Spoofingcited by 49
- 2021Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofingcited by 49
- 2021Dual Reweighting Domain Generalization for Face Presentation Attack Detectioncited by 47
- 2023Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignmentcited by 47
- 2021Generalizable Representation Learning for Mixture Domain Face Anti-Spoofingcited by 46
- 2023Instance-Aware Domain Generalization for Face Anti-Spoofingcited by 42
- 2021TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detectioncited by 41
- 2022Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofingcited by 38
- 2021Video Transformer for Deepfake Detection with Incremental Learningcited by 33
- 2024Gradient Alignment for Cross-Domain Face Anti-Spoofingcited by 26
- 2020Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Reviewcited by 25
- 2024Test-Time Domain Generalization for Face Anti-Spoofingcited by 20
- 2019Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofingcited by 19
- 2022ViTransPAD: Video Transformer using convolution and self-attention for Face Presentation Attack Detectioncited by 14
- 2021SSAN: Separable Self-Attention Network for Video Representation Learningcited by 13
- 2024Generalized Face Anti-spoofing via Finer Domain Partition and Disentangling Liveness-irrelevant Factorscited by 6
- 2023A Closer Look at Geometric Temporal Dynamics for Face Anti-Spoofingcited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.