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

文献类型：期刊论文

作者Li, Yan2,3; Wang, Ruiping2,3; Cui, Zhen1; Chen, Xilin2,3; Shan, Shiguang2,3
刊名FRONTIERS OF COMPUTER SCIENCE
出版日期2018-12-01
卷号12期号:6页码:1173-1191
ISSN号2095-2228
关键词face recognition fisher discriminant analysis fusion Gabor magnitude feature multiple face models spatial pyramid based local phase quantization
DOI10.1007/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
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.

入库方式： OAI收割

来源：计算技术研究所

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