Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection

doi:10.1109/TIFS.2019.2922241

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection
	Chen, Haonan 1; Hu, Guosheng 2; Lei, Zhen3 ; Chen, Yaowu 1; Robertson, Neil M.2; Li, Stan Z.3
发表期刊	IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
ISSN	1556-6013
	2020
卷号	15 页码:578-593
通讯作者	Chen, Yaowu(cyw@mail.bme.zju.edu.cn)
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.
关键词	Face spoofing multi-scale retinex deep learning attention model feature fusion
DOI	10.1109/TIFS.2019.2922241
关键词[WOS]	RETINEX ; SCALE ; IMAGE
收录类别	SCI
语种	英语
资助项目	Fundamental Research Funds for the Central Universities ; National Natural Science Foundation of China[61572501] ; National Natural Science Foundation of China[61872367] ; National Natural Science Foundation of China[61876178] ; National Natural Science Foundation of China[61876072] ; 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
项目资助者	National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000493566500005
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：90[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/28857
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
通讯作者	Chen, Yaowu
作者单位	1.Zhejiang Univ, Zhejiang Prov Key Lab Network Multimedia Technol, Hangzhou 310027, Zhejiang, Peoples R China 2.Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT3 9DT, Antrim, North Ireland 3.Chinese Acad Sci, Inst Automat, Beijing 100864, 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.