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

doi:10.1007/s11704-017-6275-6

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Fusing magnitude and phase features with multiple face models for robust face recognition
	Li, Yan 2,3; Wang, Ruiping 2,3; Cui, Zhen 1; Chen, Xilin 2,3; Shan, Shiguang 2,3
刊名	FRONTIERS OF COMPUTER SCIENCE
	2018-12-01
卷号	12 期号:6 页码:1173-1191
关键词	face recognition fisher discriminant analysis fusion Gabor magnitude feature multiple face models spatial pyramid based local phase quantization
ISSN号	2095-2228
DOI	10.1007/s11704-017-6275-6
英文摘要	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.
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
语种	英语
出版者	HIGHER EDUCATION PRESS
WOS记录号	WOS:000453903500010
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/3518]
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.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, ICT, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Li, Yan,Wang, Ruiping,Cui, Zhen,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,Wang, Ruiping,Cui, Zhen,Chen, Xilin,&Shan, Shiguang.(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.