Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2015

Abstract

In video based face recognition, great success has been made by representing videos as linear subspaces, which typically lie in a special type of non-Euclidean space known as Grassmann manifold. To leverage the kernel-based methods developed for Euclidean space, several recent methods have been proposed to embed the Grassmann manifold into a high dimensional Hilbert space by exploiting the well established Project Metric, which can approximate the Riemannian geometry of Grassmann manifold. Nevertheless, they inevitably introduce the drawbacks from traditional kernel-based methods such as implicit map and high computational cost to the Grassmann manifold. To overcome such limitations, we propose a novel method to learn the Projection Metric directly on Grassmann manifold rather than in Hilbert space. From the perspective of manifold learning, our method can be regarded as performing a geometry-aware dimensionality reduction from the original Grassmann manifold to a lower-dimensional, more discriminative Grassmann manifold where more favorable classification can be achieved. Experiments on several real-world video face datasets demonstrate that the proposed method yields competitive performance compared with the state-of-the-art algorithms.

Keywords

Manifolds, Yttrium, Face, Kernel, Hilbert space, Symmetric matrices

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA, June 7-12

First Page

140

Last Page

149

ISBN

9781467369640

Identifier

10.1109/CVPR.2015.7298609

Publisher

IEEE Computer Society

City or Country

Boston

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