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

doi:10.1109/TIP.2017.2746993

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

	Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets
	Wang, Wen 2,3; Wang, Ruiping 2,3; Huang, Zhiwu 1; Shan, Shiguang 2,4; Chen, Xilin 2,3
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2018
卷号	27 期号:1 页码:151-163
关键词	Statistical manifold kernel discriminative learning graph embedding gaussian distribution
ISSN号	1057-7149
DOI	10.1109/TIP.2017.2746993
英文摘要	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.
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
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000413256300011
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/6881]
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.Swiss Fed Inst Technol, Comp Vis Lab, CH-8092 Zurich, Switzerland 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 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.