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Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs).
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” TPAMI , vol. 24, no. 7, pp. 971–987, 2002
2002
Earlier work this paper cites.
J. Bigun, H. Fronthaler, and K. Kollreider, “Assuring liveness in biometric identity authentication by real-time face tracking,” in CIHSPS . IEEE, 2004
2004
Earlier work this paper cites.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in CVPR . IEEE, 2005
2005
Earlier work this paper cites.
H.-K. Jee, S.-U. Jung, and J.-H. Yoo, “Liveness detection for embedded face recognition system,” International Journal of Biological and Medical Sciences , 2006
2006
Earlier work this paper cites.
T. Ahonen, A. Hadid, and M. Pietikainen, “Face description with local binary patterns: Application to face recognition,” TPAMI , no. 12, pp. 2037–2041, 2006
2006
Earlier work this paper cites.
G. Pan, L. Sun, Z. Wu, and S. Lao, “Eyeblink-based anti-spoofing in face recognition from a generic webcamera,” in ICCV , 2007
2007
Earlier work this paper cites.
G. Zhao and M. Pietikainen, “Dynamic texture recognition using local binary patterns with an application to facial expressions,” IEEE TPAMI , vol. 29, no. 6, pp. 915–928, 2007
2007
Earlier work this paper cites.
J.-W. Li, “Eye blink detection based on multiple gabor response waves,” in ICMLC , vol. 5. IEEE, 2008, pp. 2852–2856
2008
Earlier work this paper cites.
L. Wang, X. Ding, and C. Fang, “Face live detection method based on physiological motion analysis,” Tsinghua Science & Technology , vol. 14, no. 6, pp. 685–690, 2009
2009
Earlier work this paper cites.
W. Bao, H. Li, N. Li, and W. Jiang, “A liveness detection method for face recognition based on optical flow field,” in ICASSP , 2009
2009
Earlier work this paper cites.
X. Tan, Y. Li, J. Liu, and L. Jiang, “Face liveness detection from a single image with sparse low rank bilinear discriminative model,” in ECCV . Springer, 2010
2010
Earlier work this paper cites.
T. Brox and J. Malik, “Large displacement optical flow: descriptor matching in variational motion estimation,” TPAMI , vol. 33, no. 3, pp. 500–513, 2010
2010
Earlier work this paper cites.
B. Peixoto, C. Michelassi, and A. Rocha, “Face liveness detection under bad illumination conditions,” in ICIP . IEEE, 2011
2011
Earlier work this paper cites.
T. de Freitas Pereira, A. Anjos, J. M. De Martino, and S. Marcel, “Lbp- top based countermeasure against face spoofing attacks,” in ACCV , 2012
2012
Earlier work this paper cites.
A. Ali, F. Deravi, and S. Hoque, “Liveness detection using gaze collinearity,” in ICEST . IEEE, 2012
2012
Earlier work this paper cites.
Z. Zhang, J. Yan, S. Liu, Z. Lei, D. Yi, and S. Z. Li, “A face antispoofing database with diverse attacks,” in ICB , 2012
2012
Earlier work this paper cites.
I. Chingovska, A. Anjos, and S. Marcel, “On the effectiveness of local binary patterns in face anti-spoofing,” in Biometrics Special Interest Group , 2012
2012
Earlier work this paper cites.
J. Galbally, F. Alonso-Fernandez, J. Fierrez, and J. Ortega-Garcia, “A high performance fingerprint liveness detection method based on quality related features,” Future Generation Computer Systems , vol. 28, no. 1, pp. 311–321, 2012
2012
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” The Journal of Machine Learning Research , 2012
2012
Earlier work this paper cites.
O. Kähm and N. Damer, “2d face liveness detection: An overview,” in BIOSIG . IEEE, 2012
2012
Earlier work this paper cites.
J. Komulainen, A. Hadid, and M. Pietikainen, “Context based face anti-spoofing,” in BTAS , 2013
2013
Earlier work this paper cites.
T. de Freitas Pereira, A. Anjos, J. M. De Martino, and S. Marcel, “Can face anti-spoofing countermeasures work in a real world scenario?” in ICB . IEEE, 2013, pp. 1–8
2013
Earlier work this paper cites.
N. Kose and J.-L. Dugelay, “Shape and texture based countermeasure to protect face recognition systems against mask attacks,” in CVPRW , 2013
2013
Earlier work this paper cites.
J. Connell, N. Ratha, J. Gentile, and R. Bolle, “Fake iris detection using structured light,” in ICASSP , 2013
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
N. Erdogmus and S. Marcel, “Spoofing face recognition with 3d masks,” TIFS , 2014
2014
Earlier work this paper cites.
I. Chingovska, A. R. Dos Anjos, and S. Marcel, “Biometrics evaluation under spoofing attacks,” IEEE TIFS , 2014
2014
Earlier work this paper cites.
J. Galbally and S. Marcel, “Face anti-spoofing based on general image quality assessment,” in ICPR , 2014
2014
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research , 2014
2014
Earlier work this paper cites.
A. Hadid, “Face biometrics under spoofing attacks: Vulnerabilities, countermeasures, open issues, and research directions,” in CVPRW , 2014
2014
Earlier work this paper cites.
J. Galbally, S. Marcel, and J. Fierrez, “Biometric antispoofing methods: A survey in face recognition,” IEEE Access , 2014
2014
Earlier work this paper cites.
Z. Boulkenafet, J. Komulainen, and A. Hadid, “Face anti-spoofing based on color texture analysis,” in ICIP , 2015
2015
Earlier work this paper cites.
D. Wen, H. Han, and A. K. Jain, “Face spoof detection with image distortion analysis,” TIFS , 2015
2015
Earlier work this paper cites.
A. Pinto, W. R. Schwartz, H. Pedrini, and A. de Rezende Rocha, “Using visual rhythms for detecting video-based facial spoof attacks,” TIFS , 2015
2015
Earlier work this paper cites.
R. Raghavendra, K. B. Raja, and C. Busch, “Presentation attack detection for face recognition using light field camera,” TIP , vol. 24, no. 3, pp. 1060–1075, 2015
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in CVPR , 2015
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning . PMLR, 2015
2015
Earlier work this paper cites.
Z. Xu, S. Li, and W. Deng, “Learning temporal features using lstm-cnn architecture for face anti-spoofing,” in ACPR , 2015
2015
Earlier work this paper cites.
D. Menotti, G. Chiachia, A. Pinto, W. R. Schwartz, H. Pedrini, A. X. Falcao, and A. Rocha, “Deep representations for iris, face, and fingerprint spoofing detection,” TIFS , 2015
2015
Earlier work this paper cites.
X. Li, J. Komulainen, G. Zhao, P.-C. Yuen, and M. Pietikäinen, “Generalized face anti-spoofing by detecting pulse from face videos,” in ICPR , 2016
2016
Earlier work this paper cites.
K. Patel, H. Han, and A. K. Jain, “Secure face unlock: Spoof detection on smartphones,” TIFS , 2016
2016
Earlier work this paper cites.
Boulkenafet, Zinelabidine and Komulainen, Jukka and Hadid, Abdenour, “Face antispoofing using speeded-up robust features and fisher vector encoding,” SPL , vol. 24, no. 2, pp. 141–145, 2016
2016
Earlier work this paper cites.
L. Li, X. Feng, Z. Boulkenafet, Z. Xia, M. Li, and A. Hadid, “An original face anti-spoofing approach using partial convolutional neural network,” in IPTA , 2016
2016
Earlier work this paper cites.
K. Patel, H. Han, and A. K. Jain, “Cross-database face antispoofing with robust feature representation,” in CCBR , 2016
2016
Earlier work this paper cites.
H. Steiner, A. Kolb, and N. Jung, “Reliable face anti-spoofing using multispectral swir imaging,” in ICB . IEEE, 2016
2016
Earlier work this paper cites.
A. Costa-Pazo, S. Bhattacharjee, E. Vazquez-Fernandez, and S. Marcel, “The replay-mobile face presentation-attack database,” in BIOSIG . IEEE, 2016
2016
Earlier work this paper cites.
S. Liu, P. C. Yuen, S. Zhang, and G. Zhao, “3d mask face anti-spoofing with remote photoplethysmography,” in ECCV . Springer, 2016
2016
Earlier work this paper cites.
J. Galbally and R. Satta, “Three-dimensional and two-and-a-half-dimensional face recognition spoofing using three-dimensional printed models,” IET Biometrics , 2016
2016
Earlier work this paper cites.
I. Chingovska, N. Erdogmus, A. Anjos, and S. Marcel, “Face recognition systems under spoofing attacks,” in Face Recognition Across the Imaging Spectrum . Springer, 2016, pp. 165–194
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2016
2016
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: towards real-time object detection with region proposal networks,” IEEE TPAMI , vol. 39, no. 6, pp. 1137–1149, 2016
2016
Earlier work this paper cites.
L. Feng, L.-M. Po, Y. Li, X. Xu, F. Yuan, T. C.-H. Cheung, and K.-W. Cheung, “Integration of image quality and motion cues for face anti-spoofing: A neural network approach,” Journal of Visual Communication and Image Representation , 2016
2016
Earlier work this paper cites.
X. Sun, L. Huang, and C. Liu, “Context based face spoofing detection using active near-infrared images,” in ICPR , 2016
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 ICIVC . IEEE, 2017
2017
Earlier work this paper cites.
Y. Atoum, Y. Liu, A. Jourabloo, and X. Liu, “Face anti-spoofing using patch and depth-based cnns,” in IJCB , 2017
2017
Earlier work this paper cites.
S. R. Arashloo, J. Kittler, and W. Christmas, “An anomaly detection approach to face spoofing detection: A new formulation and evaluation protocol,” IEEE access , 2017
2017
Earlier work this paper cites.
R. Ramachandra and C. Busch, “Presentation attack detection methods for face recognition systems: A comprehensive survey,” ACM Computing Surveys (CSUR) , vol. 50, no. 1, pp. 1–37, 2017
2017
Earlier work this paper cites.
I. Manjani, S. Tariyal, M. Vatsa, R. Singh, and A. Majumdar, “Detecting silicone mask-based presentation attack via deep dictionary learning,” TIFS , 2017
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 FGR , 2017
2017
Earlier work this paper cites.
A. Agarwal, D. Yadav, N. Kohli, R. Singh, M. Vatsa, and A. Noore, “Face presentation attack with latex masks in multispectral videos,” in CVPRW , 2017
2017
Earlier work this paper cites.
S. Bhattacharjee and S. Marcel, “What you can’t see can help you-extended-range imaging for 3d-mask presentation attack detection,” in BIOSIG . IEEE, 2017
2017
Earlier work this paper cites.
I. J. S. Biometrics., “Information technology–biometric presentation attack detection–part 3: testing and reporting,” 2017
2017
Earlier work this paper cites.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in CVPR , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
O. Lucena, A. Junior, V. Moia, R. Souza, E. Valle, and R. Lotufo, “Transfer learning using convolutional neural networks for face anti-spoofing,” in ICIAR , 2017
2017
Earlier work this paper cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in ICCV , 2017
2017
Earlier work this paper cites.
P. Voigt and A. Von dem Bussche, “The eu general data protection regulation (gdpr),” A Practical Guide, 1st Ed., Cham: Springer International Publishing , 2017
2017
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial Intelligence and Statistics . PMLR, 2017
2017
Earlier work this paper cites.
S. Kumar, S. Singh, and J. Kumar, “A comparative study on face spoofing attacks,” in ICCCA . IEEE, 2017
2017
Earlier work this paper cites.
D. R. Kisku and R. D. Rakshit, “Face spoofing and counter-spoofing: A survey of state-of-the-art algorithms,” Transactions on Machine Learning and Artificial Intelligence , vol. 5, no. 2, pp. 31–31, 2017
2017
Earlier work this paper cites.
L. Li, Z. Xia, L. Li, X. Jiang, X. Feng, and F. Roli, “Face anti-spoofing via hybrid convolutional neural network,” in FADS . IEEE, 2017
2017
Earlier work this paper cites.
X. Tu and Y. Fang, “Ultra-deep neural network for face anti-spoofing,” in International Conference on Neural Information Processing . Springer, 2017
2017
Earlier work this paper cites.
Y. A. U. Rehman, L. M. Po, and M. Liu, “Deep learning for face anti-spoofing: An end-to-end approach,” in SPA . IEEE, 2017, pp. 195–200
2017
Earlier work this paper cites.
Y. Liu, A. Jourabloo, and X. Liu, “Learning deep models for face anti-spoofing: Binary or auxiliary supervision,” in CVPR , 2018
2018
Earlier work this paper cites.
A. Jourabloo, Y. Liu, and X. Liu, “Face de-spoofing: Anti-spoofing via noise modeling,” in ECCV , 2018
2018
Earlier work this paper cites.
L. Li, P. L. Correia, and A. Hadid, “Face recognition under spoofing attacks: countermeasures and research directions,” IET Biometrics , vol. 7, no. 1, pp. 3–14, 2018
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,” TIFS , 2018
2018
Earlier work this paper cites.
S. Bhattacharjee, A. Mohammadi, and S. Marcel, “Spoofing deep face recognition with custom silicone masks,” in BTAS , 2018
2018
Earlier work this paper cites.
R. Shao, X. Lan, and P. C. Yuen, “Joint discriminative learning of deep dynamic textures for 3d mask face anti-spoofing,” TIFS , 2018
2018
Earlier work this paper cites.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in CVPR , 2018, pp. 4510–4520
2018
Earlier work this paper cites.
O. Nikisins, A. Mohammadi, A. Anjos, and S. Marcel, “On effectiveness of anomaly detection approaches against unseen presentation attacks in face anti-spoofing,” in ICB . IEEE, 2018
2018
Earlier work this paper cites.
F. Xiong and W. AbdAlmageed, “Unknown presentation attack detection with face rgb images,” in BTAS , 2018
2018
Earlier work this paper cites.
L. Souza, L. Oliveira, M. Pamplona, and J. Papa, “How far did we get in face spoofing detection?” Engineering Applications of Artificial Intelligence , vol. 72, pp. 368–381, 2018
2018
Cited alongside, same era.
C. Lin, Z. Liao, P. Zhou, J. Hu, and B. Ni, “Live face verification with multiple instantialized local homographic parameterization.” in IJCAI , 2018
2018
Cited alongside, same era.
G. B. de Souza, J. P. Papa, and A. N. Marana, “On the learning of deep local features for robust face spoofing detection,” in SIBGRAPI . IEEE, 2018
2018
Cited alongside, same era.
Y. A. U. Rehman, L. M. Po, and M. Liu, “Livenet: Improving features generalization for face liveness detection using convolution neural networks,” Expert Systems with Applications , vol. 108, pp. 159–169, 2018
2018
Cited alongside, same era.
S. Luo, M. Kan, S. Wu, X. Chen, and S. Shan, “Face anti-spoofing with multi-scale information,” in ICPR , 2018
Z. Wang, Z. Yu, C. Zhao, X. Zhu, Y. Qin, Q. Zhou, F. Zhou, and Z. Lei, “Deep spatial gradient and temporal depth learning for face anti-spoofing,” in CVPR , 2020
2020
Later among the works it cites.
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 ECCV . Springer, 2020
2020
Later among the works it cites.
Y. Liu, J. Stehouwer, and X. Liu, “On disentangling spoof trace for generic face anti-spoofing,” in ECCV . Springer, 2020
2020
Later among the works it cites.
X. Niu, Z. Yu, H. Han, X. Li, S. Shan, and G. Zhao, “Video-based remote physiological measurement via cross-verified feature disentangling,” in ECCV . Springer, 2020
2020
Later among the works it cites.
L. Li, Z. Xia, X. Jiang, Y. Ma, F. Roli, and X. Feng, “3d face mask presentation attack detection based on intrinsic image analysis,” IET Biometrics , 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
K. Larbi, W. Ouarda, H. Drira, B. B. Amor, and C. B. Amar, “Deepcolorfasd: Face anti spoofing solution using a multi channeled color spaces cnn,” in SMC . IEEE, 2018
2018
Cited alongside, same era.
B. Lin, X. Li, Z. Yu, and G. Zhao, “Face liveness detection by rppg features and contextual patch-based cnn,” in ICBEA . ACM, 2019
2019
Cited alongside, same era.
Z. Yu, W. Peng, X. Li, X. Hong, and G. Zhao, “Remote heart rate measurement from highly compressed facial videos: an end-to-end deep learning solution with video enhancement,” in ICCV , 2019
2019
Cited alongside, same era.
X. Song, X. Zhao, L. Fang, and T. Lin, “Discriminative representation combinations for accurate face spoofing detection,” Pattern Recognition , 2019
2019
Cited alongside, same era.
M. Khammari, “Robust face anti-spoofing using cnn with lbp and wld,” IET Image Processing , 2019
2019
Cited alongside, same era.
X. Yang, W. Luo, L. Bao, Y. Gao, D. Gong, S. Zheng, Z. Li, and W. Liu, “Face anti-spoofing: Model matters, so does data,” in CVPR , 2019
2019
Cited alongside, same era.
A. George and S. Marcel, “Deep pixel-wise binary supervision for face presentation attack detection,” in ICB , no. CONF, 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
D. Deb and A. K. Jain, “Look locally infer globally: A generalizable face anti-spoofing approach,” TIFS , 2020
2020
Later among the works it cites.
H. Ge, X. Tu, W. Ai, Y. Luo, Z. Ma, and M. Xie, “Face anti-spoofing by the enhancement of temporal motion,” in CTISC . IEEE, 2020
2020
Later among the works it cites.
A. Mohammadi, S. Bhattacharjee, and S. Marcel, “Improving cross-dataset performance of face presentation attack detection systems using face recognition datasets,” in ICASSP , 2020
2020
Later among the works it cites.
D. Peng, J. Xiao, R. Zhu, and G. Gao, “Ts-fen: Probing feature selection strategy for face anti-spoofing,” in ICASS . IEEE, 2020
2020
Later among the works it cites.
M. S. Hossain, L. Rupty, K. Roy, M. Hasan, S. Sengupta, and N. Mohammed, “A-deeppixbis: Attentional angular margin for face anti-spoofing,” 2020
2020
Later among the works it cites.
Z. Yu, Y. Qin, X. Xu, C. Zhao, Z. Wang, Z. Lei, and G. Zhao, “Auto-fas: Searching lightweight networks for face anti-spoofing,” in ICASSP , 2020
2020
Later among the works it cites.
Wang, Guoqing and Han, Hu and Shan, Shiguang and Chen, Xilin, “Unsupervised adversarial domain adaptation for cross-domain face presentation attack detection,” TIFS , 2020
2020
Later among the works it cites.
A. Mohammadi, S. Bhattacharjee, and S. Marcel, “Domain adaptation for generalization of face presentation attack detection in mobile settengs with minimal information,” in ICASSP , 2020
2020
Later among the works it cites.
H. Li, S. Wang, P. He, and A. Rocha, “Face anti-spoofing with deep neural network distillation,” IEEE Journal of Selected Topics in Signal Processing , 2020
2020
Later among the works it cites.
D. Pérez-Cabo, D. Jiménez-Cabello, A. Costa-Pazo, and R. J. López-Sastre, “Learning to learn face-pad: a lifelong learning approach,” in IJCB . IEEE, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
A. George and S. Marcel, “Learning one class representations for face presentation attack detection using multi-channel convolutional neural networks,” TIFS , 2020
2020
Later among the works it cites.
Z. Li, H. Li, K.-Y. Lam, and A. C. Kot, “Unseen face presentation attack detection with hypersphere loss,” in ICASSP , 2020
2020
Later among the works it cites.
Y. A. U. Rehman, L.-M. Po, and M. Liu, “Slnet: Stereo face liveness detection via dynamic disparity-maps and convolutional neural network,” Expert Systems with Applications , 2020
2020
Later among the works it cites.
X. Wu, J. Zhou, J. Liu, F. Ni, and H. Fan, “Single-shot face anti-spoofing for dual pixel camera,” TIFS , 2020
2020
Later among the works it cites.
H. Farrukh, R. M. Aburas, S. Cao, and H. Wang, “Facerevelio: a face liveness detection system for smartphones with a single front camera,” in Proceedings of the 26th Annual International Conference on Mobile Computing and Networking , 2020
2020
Later among the works it cites.
A. F. Ebihara, K. Sakurai, and H. Imaoka, “Specular-and diffuse-reflection-based face spoofing detection for mobile devices,” in IJCB . IEEE, 2020
2020
Later among the works it cites.
F. Jiang, P. Liu, X. Shao, and X. Zhou, “Face anti-spoofing with generated near-infrared images,” Multimedia Tools and Applications , vol. 79, no. 29, pp. 21 299–21 323, 2020
2020
Later among the works it cites.
J. Stehouwer, A. Jourabloo, Y. Liu, and X. Liu, “Noise modeling, synthesis and classification for generic object anti-spoofing,” in CVPR , 2020
2020
Later among the works it cites.
U. A. Ciftci, I. Demir, and L. Yin, “Fakecatcher: Detection of synthetic portrait videos using biological signals,” TPAMI , 2020
2020
Later among the works it cites.
J. N. Kundu, N. Venkat, R. V. Babu et al. , “Universal source-free domain adaptation,” in CVPR , 2020
2020
Later among the works it cites.
Z. Ming, M. Visani, M. M. Luqman, and J.-C. Burie, “A survey on anti-spoofing methods for facial recognition with rgb cameras of generic consumer devices,” Journal of Imaging , 2020
2020
Later among the works it cites.
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,” TIFS , vol. 16, pp. 937–951, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
X. Tu, Z. Ma, J. Zhao, G. Du, M. Xie, and J. Feng, “Learning generalizable and identity-discriminative representations for face anti-spoofing,” ACM TIST , vol. 11, no. 5, pp. 1–19, 2020
2020
Later among the works it cites.
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,” TIFS , 2020
2020
Later among the works it cites.
Y. Zuo, W. Gao, and J. Wang, “Face liveness detection algorithm based on livenesslight network,” in HPBD&IS . IEEE, 2020
2020
Later among the works it cites.
B. Chen, W. Yang, and S. Wang, “Face anti-spoofing by fusing high and low frequency features for advanced generalization capability,” in MIPR . IEEE, 2020, pp. 199–204
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
Y. Ma, L. Wu, Z. Li et al. , “A novel face presentation attack detection scheme based on multi-regional convolutional neural networks,” PR Letters , vol. 131, pp. 261–267, 2020
2020
Later among the works it cites.
W. Sun, Y. Song, H. Zhao, and Z. Jin, “A face spoofing detection method based on domain adaptation and lossless size adaptation,” IEEE Access , 2020
2020
Later among the works it cites.
S. Saha, W. Xu, M. Kanakis, S. Georgoulis, Y. Chen, D. Pani Paudel, and L. Van Gool, “Domain agnostic feature learning for image and video based face anti-spoofing,” in CVPRW , 2020
2020
Later among the works it cites.
S. Fatemifar, M. Awais, A. Akbari, and J. Kittler, “A stacking ensemble for anomaly based client-specific face spoofing detection,” in ICIP . IEEE, 2020
2020
Later among the works it cites.
S. Fatemifar, S. R. Arashloo, M. Awais, and J. Kittler, “Client-specific anomaly detection for face presentation attack detection,” Pattern Recognition , 2020
2020
Later among the works it cites.
M. Kowalski, “A study on presentation attack detection in thermal infrared,” Sensors , vol. 20, no. 14, p. 3988, 2020
2020
Later among the works it cites.
X. Li, W. Wu, T. Li, Y. Su, and L. Yang, “Face liveness detection based on parallel cnn,” in Journal of Physics: Conference Series . IOP Publishing, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
Q. Yang, X. Zhu, J.-K. Fwu, Y. Ye, G. You, and Y. Zhu, “Pipenet: Selective modal pipeline of fusion network for multi-modal face anti-spoofing,” in CVPRW , 2020
2020
Later among the works it cites.
Y. Qin, Z. Yu, L. Yan, Z. Wang, C. Zhao, and Z. Lei, “Meta-teacher for face anti-spoofing,” TPAMI , 2021
2021
Closest in time.
2021
Closest in time.
A. Liu, X. Li, J. Wan, Y. Liang, S. Escalera, H. J. Escalante, M. Madadi, Y. Jin, Z. Wu, X. Yu et al. , “Cross-ethnicity face anti-spoofing recognition challenge: A review,” IET Biometrics , 2021
2021
Closest in time.
A. Li, Z. Tan, X. Li, J. Wan, S. Escalera, G. Guo, and S. Z. Li, “Casia-surf cefa: A benchmark for multi-modal cross-ethnicity face anti-spoofing,” WACV , 2021
2021
Closest in time.
M. Rostami, L. Spinoulas, M. Hussein, J. Mathai, and W. Abd-Almageed, “Detection and continual learning of novel face presentation attacks,” in ICCV , 2021
2021
Closest in time.
Z. Yu, Y. Qin, H. Zhao, X. Li, and G. Zhao, “Dual-cross central difference network for face anti-spoofing,” in IJCAI , 2021
2021
Closest in time.
X. Xu, Y. Xiong, and W. Xia, “On improving temporal consistency for online face liveness detection,” in ICCVW , 2021
2021
Closest in time.
Y. Jia, J. Zhang, S. Shan, and X. Chen, “Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing,” Pattern Recognition , 2021
2021
Closest in time.
Z. Yu, X. Li, P. Wang, and G. Zhao, “Transrppg: Remote photoplethysmography transformer for 3d mask face presentation attack detection,” IEEE SPL , 2021
2021
Closest in time.
B. Chen, W. Yang, H. Li, S. Wang, and S. Kwong, “Camera invariant feature learning for generalized face anti-spoofing,” TIFS , 2021
2021
Closest in time.
H. Wu, D. Zeng, Y. Hu, H. Shi, and T. Mei, “Dual spoof disentanglement generation for face anti-spoofing with depth uncertainty learning,” TCSVT , 2021
2021
Closest in time.
K. Roy, M. Hasan, L. Rupty, M. Hossain, S. Sengupta, S. N. Taus, N. Mohammed et al. , “Bi-fpnfas: Bi-directional feature pyramid network for pixel-wise face anti-spoofing by leveraging fourier spectra,” Sensors , vol. 21, no. 8, p. 2799, 2021
2021
Closest in time.
R. Quan, Y. Wu, X. Yu, and Y. Yang, “Progressive transfer learning for face anti-spoofing,” TIP , vol. 30, pp. 3946–3955, 2021
2021
Closest in time.
Z. Chen, T. Yao, K. Sheng, S. Ding, Y. Tai, J. Li, F. Huang, and X. Jin, “Generalizable representation learning for mixture domain face anti-spoofing,” in AAAI , 2021
2021
Closest in time.
S. Liu, K.-Y. Zhang, T. Yao, M. Bi, S. Ding, J. Li, F. Huang, and L. Ma, “Adaptive normalized representation learning for generalizable face anti-spoofing,” in ACM MM , 2021
2021
Closest in time.
J. Wang, J. Zhang, Y. Bian, Y. Cai, C. Wang, and S. Pu, “Self-domain adaptation for face anti-spoofing,” in AAAI , 2021
2021
Closest in time.
2021
Closest in time.
A. George and S. Marcel, “Cross modal focal loss for rgbd face anti-spoofing,” in CVPR , 2021
2021
Closest in time.
W. Liu, X. Wei, T. Lei, X. Wang, H. Meng, and A. K. Nandi, “Data fusion based two-stage cascade framework for multi-modality face anti-spoofing,” TCDS , 2021
2021
Closest in time.
A. Liu, Z. Tan, J. Wan, Y. Liang, Z. Lei, G. Guo, and S. Z. Li, “Face anti-spoofing via adversarial cross-modality translation,” TIFS , 2021
2021
Closest in time.
K. Mallat and J.-L. Dugelay, “Indirect synthetic attack on thermal face biometric systems via visible-to-thermal spectrum conversion,” in CVPRW , 2021
2021
Closest in time.
A. F. Sequeira, T. Gonçalves, W. Silva, J. R. Pinto, and J. S. Cardoso, “An exploratory study of interpretability for face presentation attack detection,” IET Biometrics , 2021
2021
Closest in time.
H. Mirzaalian, M. E. Hussein, L. Spinoulas, J. May, and W. Abd-Almageed, “Explaining face presentation attack detection using natural language,” in FG . IEEE, 2021
2021
Closest in time.
A. Liu, X. Li, J. Wan, Y. Liang, S. Escalera, H. J. Escalante, M. Madadi, Y. Jin, Z. Wu, X. Yu et al. , “Cross-ethnicity face anti-spoofing recognition challenge: A review,” IET Biometrics , vol. 10, no. 1, pp. 24–43, 2021
2021
Closest in time.
S. Komkov and A. Petiushko, “Advhat: Real-world adversarial attack on arcface face id system,” in ICPR , 2021
2021
Closest in time.
B. Yin, W. Wang, T. Yao, J. Guo, Z. Kong, S. Ding, J. Li, and C. Liu, “Adv-makeup: A new imperceptible and transferable attack on face recognition,” in IJCAI , 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
R. Shao, B. Zhang, P. C. Yuen, and V. M. Patel, “Federated test-time adaptive face presentation attack detection with dual-phase privacy preservation,” in FG . IEEE, 2021
2021
Closest in time.
L. Lv, Y. Xiang, X. Li, H. Huang, R. Ruan, X. Xu, and Y. Fu, “Combining dynamic image and prediction ensemble for cross-domain face anti-spoofing,” in ICASSP , 2021
2021
Closest in time.
Z. Yu, X. Li, J. Shi, Z. Xia, and G. Zhao, “Revisiting pixel-wise supervision for face anti-spoofing,” IEEE TBIOM , 2021
2021
Closest in time.
W. Zheng, M. Yue, S. Zhao, and S. Liu, “Attention-based spatial-temporal multi-scale network for face anti-spoofing,” TBIOM , 2021
2021
Closest in time.
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 CVPR , 2022
2022
Closest in time.
Z. Wang, Z. Wang, Z. Yu, W. Deng, J. Li, S. Li, and Z. Wang, “Domain generalization via shuffled style assembly for face anti-spoofing,” in CVPR , 2022
2022
Closest in time.
Z. Wang, Q. Wang, W. Deng, and G. Guo, “Learning multi-granularity temporal characteristics for face anti-spoofing,” TIFS , 2022
2022
Closest in time.
S. Liu, S. Lu, H. Xu, J. Yang, S. Ding, and L. Ma, “Feature generation and hypothesis verification for reliable face anti-spoofing,” in AAAI , 2022
2022
Closest in time.
2022
Closest in time.
E. Sarkar, P. Korshunov, L. Colbois, and S. Marcel, “Are gan-based morphs threatening face recognition?” in ICASSP , 2022
2022
Closest in time.
G. Te, W. Hu, and Z. Guo, “Exploring hypergraph representation on face anti-spoofing beyond 2d attacks,” in ICME . IEEE, 2020
2022
Closest in time.