Projection Metric Learning on Grassmann Manifold With Application to Video Based Face Recognition

Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xilin Chen; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 140-149

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.

Related Material


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[bibtex]
@InProceedings{Huang_2015_CVPR,
author = {Huang, Zhiwu and Wang, Ruiping and Shan, Shiguang and Chen, Xilin},
title = {Projection Metric Learning on Grassmann Manifold With Application to Video Based Face Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}