中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Dual-Branch Meta-Learning Network With Distribution Alignment for Face Anti-Spoofing

文献类型：期刊论文

作者Jia, Yunpei1,2; Zhang, Jie1,2; Shan, Shiguang1,2,3
刊名IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
出版日期2022
卷号17页码:138-151
关键词Faces Feature extraction Testing Face recognition Databases Training Task analysis Face anti-spoofing face presentation attack detection domain generalization meta-learning distribution alignment deep learning
ISSN号1556-6013
DOI10.1109/TIFS.2021.3134869
英文摘要Existing face anti-spoofing (FAS) methods fail to generalize well to unseen domains with different data distribution from the training domains, due to the distribution discrepancies between various domains. To extract domain-invariant features for unseen domains, this work proposes a Dual-Branch Meta-learning Network (DBMNet) with distribution alignment for face anti-spoofing. Specifically, DBMNet consists of a feature embedding (FE) branch and a depth estimating (DE) branch for real and fake face discrimination. Each branch acts as a meta-learner and is optimized by step-adjusted meta-learning that can adaptively select the best number of meta-train steps. In order to mitigate distribution discrepancies between domains, we introduce two distribution alignment losses to directly regularize the two meta-learners, i.e., the triplet loss for FE branch and the depth loss for DE branch, respectively. Both of them are designed as part of the meta-train and meta-test objectives, which contribute to higher-order derivatives on the parameters during the meta-optimization for further seeking domain-invariant features. Extensive ablation studies and comparisons with the state-of-the-art methods show the effectiveness of our method for better generalization.
资助项目National Key Research and Development Program of China[2017YFA0700800] ; Natural Science Foundation of China[61806188] ; Natural Science Foundation of China[61976219] ; Shanghai Municipal Science and Technology Major Project[2017SHZDZX01]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000736739100004
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/18331]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Jie
作者单位1.Chinese Acad Sci, Inst Comp Technol ICT, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci UCAS, Beijing 100049, Peoples R China
3.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Jia, Yunpei,Zhang, Jie,Shan, Shiguang. Dual-Branch Meta-Learning Network With Distribution Alignment for Face Anti-Spoofing[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2022,17:138-151.
APA Jia, Yunpei,Zhang, Jie,&Shan, Shiguang.(2022).Dual-Branch Meta-Learning Network With Distribution Alignment for Face Anti-Spoofing.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,17,138-151.
MLA Jia, Yunpei,et al."Dual-Branch Meta-Learning Network With Distribution Alignment for Face Anti-Spoofing".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 17(2022):138-151.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。