Face Recognition based on Sub-pattern Sparsity Preserving Projection

DOI

10.2991/isci-15.2015.53How to use a DOI?

Keywords

Face recognition; sparse representation; sparsity preserving projection; sub-pattern sparsity preserving projection

Abstract

In order to solve the problem of pseudo approach in SPP, an unsupervised algorithm named sub-pattern sparsity preserving projection(SpSPP) was proposed in this paper. In the proposed algorithm, face images are firstly divided into smaller sub-images, and sub-images from the same location are collected to compose the sub-pattern set. Then the conventional SPP is applied to each of sub-pattern sets to extract the local features. Finally, the sub-pattern features computed by SPP are concatenated to get the holistic features. Based on the fact that different regions of face images share a different similarity relationship and the discrimination information of sparse representation, SpSPP alleviated the effect of pseudo approach through image partition and feature concatenation. The effectiveness of the proposed method was verified on popular face databases (AR and Yale B).

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qiwen Zhang
AU  - Xinlei Zhuang
PY  - 2015/01
DA  - 2015/01
TI  - Face Recognition based on Sub-pattern Sparsity Preserving Projection
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 390
EP  - 397
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.53
DO  - 10.2991/isci-15.2015.53
ID  - Zhang2015/01
ER  -