NDLI: Discriminant analysis on Riemannian manifold of Gaussian distributions for face recognition with image sets

Content Provider	IEEE Xplore Digital Library
Author	Wen Wang Ruiping Wang Zhiwu Huang Shiguang Shan Xilin Chen
Copyright Year	2015
Description	Author affiliation: Key Lab. of Intell. Inf. Process. of Chinese Acad. of Sci. (CAS), Inst. of Comput. Technol., Beijing, China (Wen Wang; Ruiping Wang; Zhiwu Huang; Shiguang Shan; Xilin Chen)
Abstract	This paper presents a method named Discriminant Analysis on Riemannian manifold of Gaussian distributions (DARG) to solve the problem of face recognition with image sets. Our goal is to capture the underlying data distribution in each set and thus facilitate more robust classification. To this end, we represent image set as Gaussian Mixture Model (GMM) comprising a number of Gaussian components with prior probabilities and seek to discriminate Gaussian components from different classes. In the light of information geometry, the Gaussians lie on a specific Riemannian manifold. To encode such Riemannian geometry properly, we investigate several distances between Gaussians and further derive a series of provably positive definite probabilistic kernels. Through these kernels, a weighted Kernel Discriminant Analysis is finally devised which treats the Gaussians in GMMs as samples and their prior probabilities as sample weights. 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.
Starting Page	2048
Ending Page	2057
File Size	523174
Page Count	10
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7298816
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Manifolds Gaussian distribution Face recognition Covariance matrices Data models Adaptation models
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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