Fusing magnitude and phase features with multiple face models for robust face recognition

doi:10.1007/s11704-017-6275-6

	Fusing magnitude and phase features with multiple face models for robust face recognition
	Li, Yan 1,2; Shan, Shiguang 1,2; Wang, Ruiping 1,2; Cui, Zhen 3; Chen, Xilin 1,2
	2018-12-01
发表期刊	FRONTIERS OF COMPUTER SCIENCE
ISSN	2095-2228
卷号	12 期号:6 页码:1173-1191
摘要	High accuracy face recognition is of great importance for a wide variety of real-world applications. Although significant progress has been made in the last decades, fully automatic face recognition systems have not yet approached the goal of surpassing the human vision system, even in controlled conditions. In this paper, we propose an approach for robust face recognition by fusing two complementary features: one is Gabor magnitude of multiple scales and orientations and the other is Fourier phase encoded by spatial pyramid based local phase quantization (SPLPQ). To reduce the high dimensionality of both features, block-wise fisher discriminant analysis (BFDA) is applied and further combined by score-level fusion. Moreover, inspired by the biological cognitive mechanism, multiple face models are exploited to further boost the robustness of the proposed approach. We evaluate the proposed approach on three challenging databases, i.e., FRGC ver2.0, LFW, and CFW-p, that address two face classification scenarios, i.e., verification and identification. Experimental results consistently exhibit the complementarity of the two features and the performance boost gained by the multiple face models. The proposed approach achieved approximately 96% verification rate when FAR was 0.1% on FRGC ver2.0 Exp.4, impressively surpassing all the best known results.
关键词	face recognition fisher discriminant analysis fusion Gabor magnitude feature multiple face models spatial pyramid based local phase quantization
DOI	10.1007/s11704-017-6275-6
收录类别	SCI
语种	英语
资助项目	National Basic Research Program of China[2015CB351802] ; National Natural Science Foundation of China[61390511] ; National Natural Science Foundation of China[61222211] ; National Natural Science Foundation of China[61379083] ; National Natural Science Foundation of China[61271445] ; Strategic Priority Research Program of the CAS[XDB02070004] ; Youth Innovation Promotion Association CAS[2015085]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:000453903500010
出版者	HIGHER EDUCATION PRESS
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/3519
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, ICT, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Li, Yan,Shan, Shiguang,Wang, Ruiping,et al. Fusing magnitude and phase features with multiple face models for robust face recognition[J]. FRONTIERS OF COMPUTER SCIENCE,2018,12(6):1173-1191.
APA	Li, Yan,Shan, Shiguang,Wang, Ruiping,Cui, Zhen,&Chen, Xilin.(2018).Fusing magnitude and phase features with multiple face models for robust face recognition.FRONTIERS OF COMPUTER SCIENCE,12(6),1173-1191.
MLA	Li, Yan,et al."Fusing magnitude and phase features with multiple face models for robust face recognition".FRONTIERS OF COMPUTER SCIENCE 12.6(2018):1173-1191.