Robust Face Recognition With Kernelized Locality-Sensitive Group Sparsity Representation

Tan, Shoubiao, Sun, Xi, Chan, Wentao, Qu, Lei and Shao, Ling (2017) Robust Face Recognition With Kernelized Locality-Sensitive Group Sparsity Representation. IEEE Transactions on Image Processing, 26 (10). pp. 4661-4668. ISSN 1057-7149

[img]
Preview
PDF (Accepted manuscript) - Submitted Version
Download (935kB) | Preview

Abstract

In this paper, a novel joint sparse representation method is proposed for robust face recognition. We embed both group sparsity and kernelized locality-sensitive constraints into the framework of sparse representation. The group sparsity constraint is designed to utilize the grouped structure information in the training data. The local similarity between test and training data is measured in the kernel space instead of the Euclidian space. As a result, the embedded nonlinear information can be effectively captured, leading to a more discriminative representation. We show that, by integrating the kernelized local-sensitivity constraint and the group sparsity constraint, the embedded structure information can be better explored, and significant performance improvement can be achieved. On the one hand, experiments on the ORL, AR, extended Yale B, and LFW data sets verify the superiority of our method. On the other hand, experiments on two unconstrained data sets, the LFW and the IJB-A, show that the utilization of sparsity can improve recognition performance, especially on the data sets with large pose variation.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Pure Connector
Date Deposited: 29 Jul 2017 05:10
Last Modified: 22 Apr 2020 14:35
URI: https://ueaeprints.uea.ac.uk/id/eprint/64308
DOI: 10.1109/TIP.2017.2716180

Actions (login required)

View Item View Item