Local variation joint representation for face recognition with single sample per person

Meng Yang, Tiancheng Song, Shiqi Yu, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Sparse representation based classification (SRC) was originally applied to multiple-training-sample face recognition with promising performance. Recently SRC has been extended to face recognition with single sample per person by using variations extracted from a generic training set as an additional common dictionary. However, the extended SRC ignored to learn a better variation dictionary and to use local region information of face images. To address this issue, we propose a local variation joint representation (LVJR) method, which learns a variation dictionary and does joint and local collaborative representation for a query image. The learned variation dictionary was required to do similar representation for the same-type facial variations, while the joint and local collaborative representation could effectively use local information of face images. Experiments on the large-scale CMU Multi-PIE and AR databases demonstrate that the proposed LVJR method achieves better results compared with the existing solutions to the single sample per person problem.

Original languageEnglish
Title of host publicationComputer Vision CCF Chinese Conference, CCCV 2015, Proceedings
EditorsLiang Wang, Hongbin Zha, Xilin Chen, Qiguang Miao
PublisherSpringer Verlag
Pages41-50
Number of pages10
ISBN (Print)9783662485699
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event1st Chinese Conference on Computer Vision, CCCV 2015 - Xian, China
Duration: 18 Sep 201520 Sep 2015

Publication series

NameCommunications in Computer and Information Science
Volume547
ISSN (Print)1865-0929

Conference

Conference1st Chinese Conference on Computer Vision, CCCV 2015
Country/TerritoryChina
CityXian
Period18/09/1520/09/15

Keywords

  • Face recognition
  • Joint representation
  • Local variation
  • Single sample per person

ASJC Scopus subject areas

  • Computer Science (all)
  • Mathematics (all)

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