Search Results for author: Zezheng Wang

Found 13 papers, 7 papers with code

Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing

1 code implementation CVPR 2022 Zhuo Wang, Zezheng Wang, Zitong Yu, Weihong Deng, Jiahong Li, Tingting Gao, Zhongyuan Wang

A novel Shuffled Style Assembly Network (SSAN) is proposed to extract and reassemble different content and style features for a stylized feature space.

Contrastive Learning Domain Generalization +1

Meta-Teacher For Face Anti-Spoofing

no code implementations12 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.

Face Anti-Spoofing Face Recognition

PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition

no code implementations25 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).

Face Recognition

Layer-Wise Adaptive Updating for Few-Shot Image Classification

no code implementations16 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.

Classification Few-Shot Image Classification +2

Multi-Modal Face Anti-Spoofing Based on Central Difference Networks

1 code implementation17 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.

Face Anti-Spoofing Face Recognition

Searching Central Difference Convolutional Networks for Face Anti-Spoofing

3 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.

Face Anti-Spoofing Face Recognition +1

Prior-Knowledge and Attention-based Meta-Learning for Few-Shot Learning

no code implementations11 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.

Few-Shot Learning

A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing

2 code implementations CVPR 2019 Shifeng Zhang, Xiaobo Wang, Ajian Liu, Chenxu Zhao, Jun Wan, Sergio Escalera, Hailin Shi, Zezheng Wang, Stan Z. Li

To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities.

Face Anti-Spoofing Face Recognition

Representation based and Attention augmented Meta learning

no code implementations19 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.

Few-Shot Learning

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