1 code implementation • 2 Mar 2023 • Jiahong Zhang, Lihong Cao, Qiuxia Lai, Binyao Li, Yunxiao Qin
Several studies in neuroscience reveal that feature restoration which fills in the occluded information and is called amodal completion is essential for human brains to recognize partially occluded images.
no code implementations • 11 Nov 2022 • Longbin Yan, Yunxiao Qin, Shumin Liu, Jie Chen
As a powerful engine, vanilla convolution has promoted huge breakthroughs in various computer tasks.
1 code implementation • 24 Nov 2021 • Zezheng Wang, Zitong Yu, Xun Wang, Yunxiao Qin, Jiahong Li, Chenxu Zhao, Zhen Lei, Xin Liu, Size Li, Zhongyuan Wang
Face anti-spoofing (FAS) plays a crucial role in securing face recognition systems.
no code implementations • 12 Nov 2021 • Yunxiao Qin, Zitong Yu, Longbin Yan, Zezheng Wang, Chenxu Zhao, Zhen Lei
The meta-teacher is trained in a bi-level optimization manner to learn the ability to supervise the PA detectors learning rich spoofing cues.
no code implementations • 13 Oct 2021 • Yunxiao Qin, Yuanhao Xiong, JinFeng Yi, Lihong Cao, Cho-Jui Hsieh
In this paper, we define a Generalized Transferable Attack (GTA) problem where the attacker doesn't know this information and is acquired to attack any randomly encountered images that may come from unknown datasets.
2 code implementations • 5 Sep 2021 • Yunxiao Qin, Yuanhao Xiong, JinFeng Yi, Cho-Jui Hsieh
In this paper, we tackle this problem from a novel angle -- instead of using the original surrogate models, can we obtain a Meta-Surrogate Model (MSM) such that attacks to this model can be easier transferred to other models?
no code implementations • 25 Jul 2021 • Qiang Meng, Xiaqing Xu, Xiaobo Wang, Yang Qian, Yunxiao Qin, Zezheng Wang, Chenxu Zhao, Feng Zhou, Zhen Lei
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., in cases of surveillance and photo-tagging).
3 code implementations • 28 Jun 2021 • Zitong Yu, Yunxiao Qin, Xiaobai Li, Chenxu Zhao, Zhen Lei, Guoying Zhao
Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs).
1 code implementation • 4 May 2021 • Zitong Yu, Yunxiao Qin, Hengshuang Zhao, Xiaobai Li, Guoying Zhao
In this paper, we propose two Cross Central Difference Convolutions (C-CDC), which exploit the difference of the center and surround sparse local features from the horizontal/vertical and diagonal directions, respectively.
no code implementations • 10 Feb 2021 • Xiaqing Xu, Qiang Meng, Yunxiao Qin, Jianzhu Guo, Chenxu Zhao, Feng Zhou, Zhen Lei
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 face image using a pre-defined template, extracting representations and comparing.
no code implementations • 3 Nov 2020 • Zitong Yu, Jun Wan, Yunxiao Qin, Xiaobai Li, Stan Z. Li, Guoying Zhao
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems.
no code implementations • 16 Jul 2020 • Yunxiao Qin, Wei-Guo Zhang, Zezheng Wang, Chenxu Zhao, Jingping Shi
LWAU is inspired by an interesting finding that compared with common deep models, the meta-learner pays much more attention to update its top layer when learning from few images.
1 code implementation • 17 Apr 2020 • Zitong Yu, Yunxiao Qin, Xiaobai Li, Zezheng Wang, Chenxu Zhao, Zhen Lei, Guoying Zhao
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks.
6 code implementations • CVPR 2020 • Zezheng Wang, Zitong Yu, Chenxu Zhao, Xiangyu Zhu, Yunxiao Qin, Qiusheng Zhou, Feng Zhou, Zhen Lei
Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing.
6 code implementations • CVPR 2020 • Zitong Yu, Chenxu Zhao, Zezheng Wang, Yunxiao Qin, Zhuo Su, Xiaobai Li, Feng Zhou, Guoying Zhao
Here we propose a novel frame level FAS method based on Central Difference Convolution (CDC), which is able to capture intrinsic detailed patterns via aggregating both intensity and gradient information.
Ranked #4 on Face Anti-Spoofing on OULU-NPU
no code implementations • 29 Apr 2019 • Yunxiao Qin, Chenxu Zhao, Xiangyu Zhu, Zezheng Wang, Zitong Yu, Tianyu Fu, Feng Zhou, Jingping Shi, Zhen Lei
Therefore, we define face anti-spoofing as a zero- and few-shot learning problem.
no code implementations • 11 Dec 2018 • Yunxiao Qin, WeiGuo Zhang, Chenxu Zhao, Zezheng Wang, Xiangyu Zhu, Guo-Jun Qi, Jingping Shi, Zhen Lei
In this paper, inspired by the human cognition process which utilizes both prior-knowledge and vision attention in learning new knowledge, we present a novel paradigm of meta-learning approach with three developments to introduce attention mechanism and prior-knowledge for meta-learning.
no code implementations • 19 Nov 2018 • Yunxiao Qin, Chenxu Zhao, Zezheng Wang, Junliang Xing, Jun Wan, Zhen Lei
The method RAML aims to give the Meta learner the ability of leveraging the past learned knowledge to reduce the dimension of the original input data by expressing it into high representations, and help the Meta learner to perform well.
1 code implementation • 13 Nov 2018 • Zezheng Wang, Chenxu Zhao, Yunxiao Qin, Qiusheng Zhou, Guo-Jun Qi, Jun Wan, Zhen Lei
Face anti-spoofing is significant to the security of face recognition systems.