102 citations · 231 across the 18 of their papers we have counts for
17 papers · 1 filter
FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing
Ajian Liu, Zichang Tan, Zitong Yu +7
The availability of handy multi-modal (i.e., RGB-D) sensors has brought about a surge of face anti-spoofing research. However, the current multi-modal face presentation attack dete…
Surveillance Face Anti-spoofing
Hao Fang, Ajian Liu, Jun Wan +5
Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i…
Face Presentation Attack Detection
Zitong Yu, Chenxu Zhao, Zhen Lei
Face recognition technology has been widely used in daily interactive applications such as checking-in and mobile payment due to its convenience and high accuracy. However, its vul…
Flexible-Modal Face Anti-Spoofing: A Benchmark
Zitong Yu, Ajian Liu, Chenxu Zhao +3
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks. Benefitted from the maturing camera sensors, single-modal (RGB) and mult…
Makeup216: Logo Recognition with Adversarial Attention Representations
Junjun Hu, Yanhao Zhu, Bo Zhao +5
One of the challenges of logo recognition lies in the diversity of forms, such as symbols, texts or a combination of both; further, logos tend to be extremely concise in design whi…
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.…
3D High-Fidelity Mask Face Presentation Attack Detection Challenge
Ajian Liu, Chenxu Zhao, Zitong Yu +8
The threat of 3D masks to face recognition systems is increasingly serious and has been widely concerned by researchers. To facilitate the study of the algorithms, a large-scale Hi…
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.…
Deep Learning for Face Anti-Spoofing: A Survey
Zitong Yu, Yunxiao Qin, Xiaobai Li +3
Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more real…
Searching for Alignment in Face Recognition
Xiaqing Xu, Qiang Meng, Yunxiao Qin +4
A standard pipeline of current face recognition frameworks consists of four individual steps: locating a face with a rough bounding box and several fiducial landmarks, aligning the…
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…
Learning Meta Face Recognition in Unseen Domains
Jianzhu Guo, Xiangyu Zhu, Chenxu Zhao +3
Face recognition systems are usually faced with unseen domains in real-world applications and show unsatisfactory performance due to their poor generalization. For example, a well-…
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…
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…