Single-sample face recognition based on wssrc and expanding sample

Zhijing Xu, Li Ye, Xiangjian He

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


This paper proposes a face recognition method with one training image per person, and it is based on compressed sensing. We apply nonlinear dimensionality reduction through locally linear embedding and sparse coefficients to generate multiple samples of each person. These generated samples have multi-expressions and multi-gestures are added to the original sample set for training. Then, a super sparse random projection and weighted optimization are applied to improve the SRC. This proposed method is named weighted super sparse representation classification (WSSRC) and is used for face recognition in this paper. Experiments on the well-known ORL face dataset and FERET face dataset show that WSSRC is about 15.53 % and 7.67 %, respectively, more accurate than the original SRC method in the context of single sample face recognition problem. In addition, extensive experimental results reported in this paper show that WSSRC also achieve higher recognition rates than RSRC, SSRC DMMA, and DCT-based DMMA.

Original languageEnglish
Title of host publicationMulti-disciplinary Trends in Artificial Intelligence - 9th International Workshop, MIWAI 2015, Proceedings
EditorsXianghan Zheng, Antonis Bikakis
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319261805
Publication statusPublished - 2015
Externally publishedYes
Event9th International Workshop on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2015 - Fuzhou, China
Duration: 13 Nov 201515 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Workshop on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2015


  • Local neighborhood embedding
  • Nonlinear dimensionality reduction
  • Single sample
  • Sparse representation classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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