Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection
文献类型:期刊论文
作者 | Chen, Haonan3; Hu, Guosheng1; Lei, Zhen2; Chen, Yaowu3; Robertson, Neil M.1; Li, Stan Z.2 |
刊名 | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY |
出版日期 | 2020 |
卷号 | 15页码:578-593 |
ISSN号 | 1556-6013 |
关键词 | Face spoofing multi-scale retinex deep learning attention model feature fusion |
DOI | 10.1109/TIFS.2019.2922241 |
通讯作者 | Chen, Yaowu(cyw@mail.bme.zju.edu.cn) |
英文摘要 | Since the human face preserves the richest information for recognizing individuals, face recognition has been widely investigated and achieved great success in various applications in the past decades. However, face spoofing attacks (e.g., face video replay attack) remain a threat to modern face recognition systems. Though many effective methods have been proposed for anti-spoofing, we find that the performance of many existing methods is degraded by illuminations. It motivates us to develop illumination-invariant methods for anti-spoofing. In this paper, we propose a two-stream convolutional neural network (TSCNN), which works on two complementary spaces: RGB space (original imaging space) and multi-scale retinex (MSR) space (illumination-invariant space). Specifically, the RGB space contains the detailed facial textures, yet it is sensitive to illumination; MSR is invariant to illumination, yet it contains less detailed facial information. In addition, the MSR images can effectively capture the high-frequency information, which is discriminative for face spoofing detection. Images from two spaces are fed to the TSCNN to learn the discriminative features for anti-spoofing. To effectively fuse the features from two sources (RGB and MSR), we propose an attention-based fusion method, which can effectively capture the complementarity of two features. We evaluate the proposed framework on various databases, i.e., CASIA-FASD, REPLAY-ATTACK, and OULU, and achieve very competitive performance. To further verify the generalization capacity of the proposed strategies, we conduct cross-database experiments, and the results show the great effectiveness of our method. |
WOS关键词 | RETINEX ; SCALE ; IMAGE |
资助项目 | National Natural Science Foundation of China[61876072] ; National Natural Science Foundation of China[61876178] ; National Natural Science Foundation of China[61872367] ; National Natural Science Foundation of China[61572501] ; Fundamental Research Funds for the Central Universities |
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:000493566500005 |
资助机构 | National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities |
源URL | [http://ir.ia.ac.cn/handle/173211/28856] |
专题 | 自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心 |
通讯作者 | Chen, Yaowu |
作者单位 | 1.Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT3 9DT, Antrim, North Ireland 2.Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China 3.Zhejiang Univ, Zhejiang Prov Key Lab Network Multimedia Technol, Hangzhou 310027, Zhejiang, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Haonan,Hu, Guosheng,Lei, Zhen,et al. Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2020,15:578-593. |
APA | Chen, Haonan,Hu, Guosheng,Lei, Zhen,Chen, Yaowu,Robertson, Neil M.,&Li, Stan Z..(2020).Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,15,578-593. |
MLA | Chen, Haonan,et al."Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 15(2020):578-593. |
入库方式: OAI收割
来源:自动化研究所
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