Knowledge Commons of Institute of Automation,CAS
Face Anti-spoofing via Adversarial Cross-modality Translation | |
Ajian, Liu1; Zichang, Tan3![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,
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2021 | |
卷号 | 16页码:2759-2772 |
摘要 | Face Presentation Attack Detection (PAD) approaches based on multi-modal data have been attracted increasingly by the research community. However, they require multi-modal face data consistently involved in both the training and testing phases. It would severely limit the applicability due to the most Face Anti-spoofing (FAS) systems are only equipped with Visible (VIS) imaging devices, i.e., RGB cameras. Therefore, how to use other modality (i.e., Near-Infrared (NIR)) to assist the performance improvement of VIS-based PAD is significant for FAS. In this work, we first discuss the big gap of performances among different modalities even though the same backbone network is applied. Then, we propose a novel Cross-modal Auxiliary (CMA) framework for the VIS-based FAS task. The main trait of CMA is that the performance can be greatly improved with the help of other modality while no other modality is required in the testing stage. The proposed CMA consists of a Modality Translation Network (MT-Net) and a Modality Assistance Network (MA-Net). The former aims to close the visible gap between different modalities via a generative model that maps inputs from one modality (i.e., RGB) to another (i.e., NIR). The latter focuses on how to use the translated modality (i.e., target modality) and RGB modality (i.e., source modality) together to train a discriminative PAD model. Extensive experiments are conducted to demonstrate that the proposed framework can push the state-of-the-art (SOTA) performances on both multi-modal datasets (i.e., CASIA-SURF, CeFA, and WMCA) and RGB-based datasets (i.e., OULU-NPU, and SiW). |
收录类别 | SCIE |
语种 | 英语 |
是否为代表性论文 | 否 |
七大方向——子方向分类 | 模式识别基础 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57116 |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Jun, Wan; Yanyan, liang |
作者单位 | 1.Macau University of Science and Technology 2.Institute of Automation Chinese Academy of Sciences 3.Baidu Research 4.Westlake University |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Ajian, Liu,Zichang, Tan,Jun, Wan,et al. Face Anti-spoofing via Adversarial Cross-modality Translation[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,,2021,16:2759-2772. |
APA | Ajian, Liu.,Zichang, Tan.,Jun, Wan.,Yanyan, liang.,Zhen, Lei.,...&Stan, Z., Li.(2021).Face Anti-spoofing via Adversarial Cross-modality Translation.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,,16,2759-2772. |
MLA | Ajian, Liu,et al."Face Anti-spoofing via Adversarial Cross-modality Translation".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 16(2021):2759-2772. |
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(已压缩)TIFS2021-spoof.(2810KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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