Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2015

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.

Keywords

Gaussian distribution, graph embedding, kernel discriminative learning, statistical manifold

Discipline

Artificial Intelligence and Robotics

Research Areas

Data Science and Engineering

Publication

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

First Page

2048

Last Page

2057

ISBN

9781467369640

Identifier

10.1109/CVPR.2015.7298816

Publisher

IEEE Computer Society

City or Country

Boston

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