Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing
文献类型：期刊论文
| 作者 | Jia, Yunpei1,2; Zhang, Jie1,2; Shan, Shiguang1,2,3; Chen, Xilin1,2 |
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2021-07-01 |
| 卷号 | 115页码:13 |
| 关键词 | Face anti-spoofing Face presentation attack detection Domain adaptation Deep learning |
| ISSN号 | 0031-3203 |
| DOI | 10.1016/j.patcog.2021.107888 |
| 英文摘要 | Due to the environmental differences, many face anti-spoofing methods fail to generalize to unseen scenarios. In light of this, we propose a unified unsupervised and semi-supervised domain adaptation network (USDAN) for cross-scenario face anti-spoofing, aiming at minimizing the distribution discrepancy between the source and the target domains. Specifically, two modules, i.e., marginal distribution alignment module (MDA) and conditional distribution alignment module (CDA), are designed to seek a domain-invariant feature space via adversarial learning and make the features of the same class compact, respectively. By adding/removing the CDA module, the network can be easily switched for semisupervised/unsupervised setting, in which sense our method is named with & ldquo;unified & rdquo;. Moreover, the adaptive cross-entropy loss and normalization techniques are further incorporated to improve the generalization. Extensive experimental results show that the proposed USDAN outperforms state-of-the-art methods on several public datasets. (c) 2021 Elsevier Ltd. All rights reserved. |
| 资助项目 | National Key R&D Program of China[2018AAA0102402] ; Natural Science Foundation of China[61806188] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000639745600001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/16680]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Shan, Shiguang |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Chinese Acad Sci CAS, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jia, Yunpei,Zhang, Jie,Shan, Shiguang,et al. Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing[J]. PATTERN RECOGNITION,2021,115:13. |
| APA | Jia, Yunpei,Zhang, Jie,Shan, Shiguang,&Chen, Xilin.(2021).Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing.PATTERN RECOGNITION,115,13. |
| MLA | Jia, Yunpei,et al."Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing".PATTERN RECOGNITION 115(2021):13. |
入库方式： OAI收割
来源：计算技术研究所
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