102 citations · 349 across the 13 of their papers we have counts for
13 papers
Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing
Zhuo Wang, Zezheng Wang, Zitong Yu +4
With diverse presentation attacks emerging continually, generalizable face anti-spoofing (FAS) has drawn growing attention. Most existing methods implement domain generalization (D…
Consistency Regularization for Deep Face Anti-Spoofing
Zezheng Wang, Zitong Yu, Xun Wang +7
Face anti-spoofing (FAS) plays a crucial role in securing face recognition systems. Empirically, given an image, a model with more consistent output on different views of this imag…
Meta-Teacher For Face Anti-Spoofing
Yunxiao Qin, Zitong Yu, Longbin Yan +3
Face anti-spoofing (FAS) secures face recognition from presentation attacks (PAs). Existing FAS methods usually supervise PA detectors with handcrafted binary or pixel-wise labels.…
PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition
Qiang Meng, Xiaqing Xu, Xiaobo Wang +6
Despite the great success achieved by deep learning methods in face recognition, severe performance drops are observed for large pose variations in unconstrained environments (e.g.…
Layer-Wise Adaptive Updating for Few-Shot Image Classification
Yunxiao Qin, Weiguo Zhang, Zezheng Wang +2
Few-shot image classification (FSIC), which requires a model to recognize new categories via learning from few images of these categories, has attracted lots of attention. Recently…
Multi-Modal Face Anti-Spoofing Based on Central Difference Networks
Zitong Yu, Yunxiao Qin, Xiaobai Li +4
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks. Existing multi-modal FAS methods rely on stacked vanilla convolutions, w…
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
Zezheng Wang, Zitong Yu, Chenxu Zhao +5
Face anti-spoofing is critical to the security of face recognition systems. Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing. D…
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
Zitong Yu, Chenxu Zhao, Zezheng Wang +5
Face anti-spoofing (FAS) plays a vital role in face recognition systems. Most state-of-the-art FAS methods 1) rely on stacked convolutions and expert-designed network, which is wea…
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing
Yunxiao Qin, Chenxu Zhao, Xiangyu Zhu +6
Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various pre…
Prior-Knowledge and Attention-based Meta-Learning for Few-Shot Learning
Yunxiao Qin, Weiguo Zhang, Chenxu Zhao +5
Recently, meta-learning has been shown as a promising way to solve few-shot learning. In this paper, inspired by the human cognition process which utilizes both prior-knowledge and…
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing
Shifeng Zhang, Xiaobo Wang, Ajian Liu +6
Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmar…
Representation based and Attention augmented Meta learning
Yunxiao Qin, Chenxu Zhao, Zezheng Wang +3
Deep learning based computer vision fails to work when labeled images are scarce. Recently, Meta learning algorithm has been confirmed as a promising way to improve the ability of…
Exploiting temporal and depth information for multi-frame face anti-spoofing
Zezheng Wang, Chenxu Zhao, Yunxiao Qin +4
Face anti-spoofing is significant to the security of face recognition systems. Previous works on depth supervised learning have proved the effectiveness for face anti-spoofing. Nev…