Fetching the paper…
Reading the bibliography…
Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face recognition system by presenting spoofed faces.
X. Tan, Y. Li, J. Liu, and L. Jiang, “Face Liveness Detection from a Single Image with Sparse Low Rank Bilinear Discriminative Model,” in European Conference on Computer Vision
2010
Earlier work this paper cites.
J. Määttä, A. Hadid, and M. Pietikäinen, “Face spoofing detection from single images using micro-texture analysis,” in 2011 International Joint Conference on Biometrics (IJCB)
2011
Earlier work this paper cites.
I. Chingovska, A. Anjos, and S. Marcel, “On the effectiveness of local binary patterns in face anti-spoofing,” in 2012 BIOSIG - Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG)
2012
Earlier work this paper cites.
Z. Zhang, J. Yan, S. Liu, Z. Lei, D. Yi, and S. Z. Li, “A face anti-spoofing database with diverse attacks,” in IAPR International Conference on Biometrics
2012
Earlier work this paper cites.
J. Komulainen, A. Hadid, and M. Pietikäinen, “Context based face anti-spoofing,” in 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)
2013
Earlier work this paper cites.
J. Galbally and S. Marcel, “Face Anti-spoofing Based on General Image Quality Assessment,” in 2014 22nd International Conference on Pattern Recognition
2014
Earlier work this paper cites.
T. de Freitas Pereira, J. Komulainen, A. Anjos, J. M. De Martino, A. Hadid, M. Pietikäinen, and S. Marcel, “Face liveness detection using dynamic texture,” EURASIP Journal on Image and Video Processing
2014
Earlier work this paper cites.
D. Wen, H. Han, and A. K. Jain, “Face Spoof Detection With Image Distortion Analysis,” IEEE Transactions on Information Forensics and Security
2015
Earlier work this paper cites.
Z. Boulkenafet, J. Komulainen, and A. Hadid, “Face Spoofing Detection Using Colour Texture Analysis,” IEEE Transactions on Information Forensics and Security
2016
Earlier work this paper cites.
H. Li, S. Wang, and A. C. Kot, “Face spoofing detection with image quality regression,” in 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)
2016
Earlier work this paper cites.
J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14
2016
Earlier work this paper cites.
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks,” IEEE Signal Processing Letters
2016
Earlier work this paper cites.
M. Asim, Z. Ming, and M. Y. Javed, “CNN based spatio-temporal feature extraction for face anti-spoofing,” in 2017 2nd International Conference on Image, Vision and Computing (ICIVC)
2017
Earlier work this paper cites.
Z. Boulkenafet, J. Komulainen, L. Li, X. Feng, and A. Hadid, “OULU-NPU: A mobile face presentation attack database with real-world variations,” in IEEE International Conference on Automatic Face and Gesture Recognition
2017
Earlier work this paper cites.
H. Li, P. He, S. Wang, A. Rocha, X. Jiang, and A. C. Kot, “Learning Generalized Deep Feature Representation for Face Anti-Spoofing,” IEEE Transactions on Information Forensics and Security
2018
Earlier work this paper cites.
Y. Liu, A. Jourabloo, and X. Liu, “Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2018
Earlier work this paper cites.
H. Li, W. Li, H. Cao, S. Wang, F. Huang, and A. C. Kot, “Unsupervised Domain Adaptation for Face Anti-Spoofing,” IEEE Transactions on Information Forensics and Security
2018
Earlier work this paper cites.
H. Li, S. J. Pan, S. Wang, and A. C. Kot, “Domain Generalization with Adversarial Feature Learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2018
Earlier work this paper cites.
Z. Pan, B. Zhuang, H. He, J. Liu, and J. Cai, “Less is more: Pay less attention in vision transformers,” in Proceedings of the AAAI Conference on Artificial Intelligence
2018
Earlier work this paper cites.
R. Shao, X. Lan, J. Li, and P. C. Yuen, “Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. George and S. Marcel, “Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection,” in 2019 International Conference on Biometrics (ICB)
2019
Earlier work this paper cites.
L. Li, Z. Xia, A. Hadid, X. Jiang, H. Zhang, and X. Feng, “Replayed Video Attack Detection Based on Motion Blur Analysis,” IEEE Transactions on Information Forensics and Security
2019
Earlier work this paper cites.
Y. A. U. Rehman, L.-M. Po, M. Liu, Z. Zou, and W. Ou, “Perturbing Convolutional Feature Maps with Histogram of Oriented Gradients for Face Liveness Detection,” in International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019)
2019
Earlier work this paper cites.
B. Liu, Z. Wu, H. Hu, and S. Lin, “Deep metric transfer for label propagation with limited annotated data,” in ICCV Workshop
2019
Earlier work this paper cites.
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International Conference on Machine Learning
2019
Earlier work this paper cites.
Y. Liu, J. Stehouwer, A. Jourabloo, and X. Liu, “Deep Tree Learning for Zero-Shot Face Anti-Spoofing,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2019
Cited alongside, same era.
R. Cai, H. Li, S. Wang, C. Chen, and A. C. Kot, “DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing,” IEEE Transactions on Information Forensics and Security
2020
Cited alongside, same era.
Y. Jia, J. Zhang, S. Shan, and X. Chen, “Single-Side Domain Generalization for Face Anti-Spoofing,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2020
Cited alongside, same era.
R. Shao, X. Lan, and P. C. Yuen, “Regularized Fine-Grained Meta Face Anti-Spoofing,” Proceedings of the AAAI Conference on Artificial Intelligence
2020
Cited alongside, same era.
H. Wu, D. Zeng, Y. Hu, H. Shi, and T. Mei, “Dual spoof disentanglement generation for face anti-spoofing with depth uncertainty learning,” IEEE Transactions on Circuits and Systems for Video Technology
2021
Later among the works it cites.
2021
Later among the works it cites.
Z. Yu, Y. Qin, H. Zhao, X. Li, and G. Zhao, “Dual-Cross Central Difference Network for Face Anti-Spoofing,” in 2021 International Joint Conference on Artificial Intelligence (IJCAI)
2021
Later among the works it cites.
Z. Yu, Y. Qin, X. Li, C. Zhao, Z. Lei, and G. Zhao, “Deep learning for face anti-spoofing: A survey,” IEEE transactions on pattern analysis and machine intelligence
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
G. Wang, H. Han, S. Shan, and X. Chen, “Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2020
Cited alongside, same era.
Y. Liu, J. Stehouwer, and X. Liu, “On disentangling spoof trace for generic face anti-spoofing,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVIII 16
2020
Cited alongside, same era.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al
2020
Cited alongside, same era.
W. Sun, Y. Song, C. Chen, J. Huang, and A. C. Kot, “Face Spoofing Detection Based on Local Ternary Label Supervision in Fully Convolutional Networks,” IEEE Transactions on Information Forensics and Security
2020
Cited alongside, same era.
A. Pinto, S. Goldenstein, A. Ferreira, T. Carvalho, H. Pedrini, and A. Rocha, “Leveraging Shape, Reflectance and Albedo From Shading for Face Presentation Attack Detection,” IEEE Transactions on Information Forensics and Security
2020
Cited alongside, same era.
H. Chen, G. Hu, Z. Lei, Y. Chen, N. M. Robertson, and S. Z. Li, “Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection,” IEEE Transactions on Information Forensics and Security
2020
Cited alongside, same era.
K.-Y. Zhang, T. Yao, J. Zhang, Y. Tai, S. Ding, J. Li, F. Huang, H. Song, and L. Ma, “Face anti-spoofing via disentangled representation learning,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIX 16
2020
Cited alongside, same era.
A. George and S. Marcel, “Learning one class representations for face presentation attack detection using multi-channel convolutional neural networks,” IEEE Transactions on Information Forensics and Security
2020
Cited alongside, same era.
2022
Later among the works it cites.
C. Kong, K. Zheng, S. Wang, A. Rocha, and H. Li, “Beyond the pixel world: A novel acoustic-based face anti-spoofing system for smartphones,” IEEE Transactions on Information Forensics and Security
2022
Later among the works it cites.
C. Kong, S. Wang, H. Li, et al
2022
Later among the works it cites.
Z. Wang, Z. Wang, Z. Yu, W. Deng, J. Li, T. Gao, and Z. Wang, “Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2022
Later among the works it cites.
R. Cai, Z. Li, R. Wan, H. Li, Y. Hu, and A. C. Kot, “Learning Meta Pattern for Face Anti-Spoofing,” IEEE Transactions on Information Forensics and Security
2022
Later among the works it cites.
Y. Liu, Y. Chen, W. Dai, C. Li, J. Zou, and H. Xiong, “Causal intervention for generalizable face anti-spoofing,” in ICME
2022
Later among the works it cites.
H.-P. Huang, D. Sun, Y. Liu, W.-S. Chu, T. Xiao, J. Yuan, H. Adam, and M.-H. Yang, “Adaptive transformers for robust few-shot cross-domain face anti-spoofing,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XIII
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Liu, Y. Chen, W. Dai, M. Gou, C.-T. Huang, and H. Xiong, “Source-free domain adaptation with contrastive domain alignment and self-supervised exploration for face anti-spoofing,” in ECCV
2022
Later among the works it cites.
W. Yan, Y. Zeng, and H. Hu, “Domain adversarial disentanglement network with cross-domain synthesis for generalized face anti-spoofing,” IEEE Transactions on Circuits and Systems for Video Technology
2022
Later among the works it cites.
A. Liu, C. Zhao, Z. Yu, J. Wan, A. Su, X. Liu, Z. Tan, S. Escalera, J. Xing, Y. Liang, et al
2022
Later among the works it cites.
Z. Li, R. Cai, H. Li, K.-Y. Lam, Y. Hu, and A. C. Kot, “One-class knowledge distillation for face presentation attack detection,” IEEE Transactions on Information Forensics and Security
2022
Later among the works it cites.
M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim, “Visual prompt tuning,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXIII
2022
Later among the works it cites.
J. Peeples, W. Xu, and A. Zare, “Histogram layers for texture analysis,” IEEE Transactions on Artificial Intelligence
2022
Later among the works it cites.
C.-Y. Wang, Y.-D. Lu, S.-T. Yang, and S.-H. Lai, “PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2022
Later among the works it cites.
Q. Zhou, K.-Y. Zhang, T. Yao, R. Yi, S. Ding, and L. Ma, “Adaptive mixture of experts learning for generalizable face anti-spoofing,” in Proceedings of the 30th ACM International Conference on Multimedia
2022
Later among the works it cites.
Z. Chen, Y. Duan, W. Wang, J. He, T. Lu, J. Dai, and Y. Qiao, “Vision transformer adapter for dense predictions,” in ICLR
2023
Closest in time.
G. Zheng, Y. Liu, W. Dai, C. Li, J. Zou, and H. Xiong, “Learning causal representations for generalizable face anti spoofing,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2023
Closest in time.
2023
Closest in time.
Z. Wang, Z. Yu, X. Wang, Y. Qin, J. Li, C. Zhao, X. Liu, and Z. Lei, “Consistency regularization for deep face anti-spoofing,” IEEE Transactions on Information Forensics and Security
2023
Closest in time.
C.-H. Liao, W.-C. Chen, H.-T. Liu, Y.-R. Yeh, M.-C. Hu, and C.-S. Chen, “Domain invariant vision transformer learning for face anti-spoofing,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision
2023
Closest in time.
X. Lin, S. Wang, R. Cai, Y. Liu, Y. Fu, W. Tang, Z. Yu, and A. Kot, “Suppress and rebalance: Towards generalized multi-modal face anti-spoofing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2024
Closest in time.