Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets

doi:10.1109/TIP.2017.2746993

	Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets
	Wang, Wen 1,2; Wang, Ruiping 1,2; Huang, Zhiwu 3; Shan, Shiguang 1,4; Chen, Xilin 1,2
	2018
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	27 期号:1 页码:151-163
摘要	To address the problem of face recognition with image sets, we aim to capture the underlying data distribution in each set and thus facilitate more robust classification. To this end, we represent image set as the Gaussian mixture model (GMM) comprising a number of Gaussian components with prior probabilities and seek to discriminate Gaussian components from different classes. Since in the light of information geometry, the Gaussians lie on a specific Riemannian manifold, this paper presents a method named discriminant analysis on Riemannian manifold of Gaussian distributions (DARG). We investigate several distance metrics between Gaussians and accordingly two discriminative learning frameworks are presented to meet the geometric and statistical characteristics of the specific manifold. The first framework derives a series of provably positive definite probabilistic kernels to embed the manifold to a high-dimensional Hilbert space, where conventional discriminant analysis methods developed in Euclidean space can be applied, and a weighted Kernel discriminant analysis is devised which learns discriminative representation of the Gaussian components in GMMs with their prior probabilities as sample weights. Alternatively, the other framework extends the classical graph embedding method to the manifold by utilizing the distance metrics between Gaussians to construct the adjacency graph, and hence the original manifold is embedded to a lower-dimensional and discriminative target manifold with the geometric structure preserved and the interclass separability maximized. The proposed method is evaluated by face identification and verification tasks on four most challenging and largest databases, YouTube Celebrities, COX, YouTube Face DB, and Point-and-Shoot Challenge, to demonstrate its superiority over the state-of-the-art.
关键词	Statistical manifold kernel discriminative learning graph embedding gaussian distribution
DOI	10.1109/TIP.2017.2746993
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[61390511] ; Natural Science Foundation of China[61379083] ; Natural Science Foundation of China[61650202] ; Natural Science Foundation of China[61672496] ; 973 Program[2015CB351802] ; Youth Innovation Promotion Association, CAS[2015085]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000413256300011
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：45[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/2XEOYT63/6882
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Ruiping
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Swiss Fed Inst Technol, Comp Vis Lab, CH-8092 Zurich, Switzerland 4.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
推荐引用方式 GB/T 7714	Wang, Wen,Wang, Ruiping,Huang, Zhiwu,et al. Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2018,27(1):151-163.
APA	Wang, Wen,Wang, Ruiping,Huang, Zhiwu,Shan, Shiguang,&Chen, Xilin.(2018).Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets.IEEE TRANSACTIONS ON IMAGE PROCESSING,27(1),151-163.
MLA	Wang, Wen,et al."Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets".IEEE TRANSACTIONS ON IMAGE PROCESSING 27.1(2018):151-163.