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Unsupervised Adversarial Domain Adaptation for Cross-Domain Face Presentation Attack Detection

文献类型：期刊论文

作者Wang, Guoqing1,2; Han, Hu1,3; Shan, Shiguang1,2,4; Chen, Xilin1,2
刊名IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
出版日期2021
卷号16页码:56-69
ISSN号1556-6013
关键词Face Feature extraction Testing Adaptation models Databases Deep learning Three-dimensional displays Face presentation attack detection face liveness detection face anti-spoofing adversarial domain adaptation metric learning disentangled representation
DOI10.1109/TIFS.2020.3002390
英文摘要Face presentation attack detection (PAD) is essential for securing the widely used face recognition systems. Most of the existing PAD methods do not generalize well to unseen scenarios because labeled training data of the new domain is usually not available. In light of this, we propose an unsupervised domain adaptation with disentangled representation (DR-UDA) approach to improve the generalization capability of PAD into new scenarios. DR-UDA consists of three modules, i.e., ML-Net, UDA-Net and DR-Net. ML-Net aims to learn a discriminative feature representation using the labeled source domain face images via metric learning. UDA-Net performs unsupervised adversarial domain adaptation in order to optimize the source domain and target domain encoders jointly, and obtain a common feature space shared by both domains. As a result, the source domain PAD model can be effectively transferred to the unlabeled target domain for PAD. DR-Net further disentangles the features irrelevant to specific domains by reconstructing the source and target domain face images from the common feature space. Therefore, DR-UDA can learn a disentangled representation space which is generative for face images in both domains and discriminative for live vs. spoof classification. The proposed approach shows promising generalization capability in several public-domain face PAD databases.
资助项目National Key Research and Development Program of China[2017YFA0700804] ; Natural Science Foundation of China[61672496]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000554454600005
源URL[http://119.78.100.204/handle/2XEOYT63/15848]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Han, Hu
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Peng Cheng Lab, Shenzhen 518055, Peoples R China
4.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Guoqing,Han, Hu,Shan, Shiguang,et al. Unsupervised Adversarial Domain Adaptation for Cross-Domain Face Presentation Attack Detection[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2021,16:56-69.
APA Wang, Guoqing,Han, Hu,Shan, Shiguang,&Chen, Xilin.(2021).Unsupervised Adversarial Domain Adaptation for Cross-Domain Face Presentation Attack Detection.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,16,56-69.
MLA Wang, Guoqing,et al."Unsupervised Adversarial Domain Adaptation for Cross-Domain Face Presentation Attack Detection".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 16(2021):56-69.

入库方式： OAI收割

来源：计算技术研究所

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