Virtual samples construction using image-block-stretching for face recognition

Yingnan Zhao, Xiangjian He, Beijing Chen

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


Face recognition encounters the problem that multiple samples of the same object may be very different owing to the deformation of appearances. To synthesizing reasonable virtual samples is a good way to solve it. In this paper, we introduce the idea of image-block-stretching to generate virtual images for deformable faces. It allows the neighbored image blocks to be stretching randomly to reflect possible variations of the appearance of faces. We demonstrate that virtual images obtained using image-block-stretching and original images are complementary in representing faces. Extensive classification experiments on face databases show that the proposed virtual image scheme is very competent and can be combined with a number of classifiers, such as the sparse representation classification, to achieve surprising accuracy improvement.

Original languageEnglish
Title of host publicationDatabases Theory and Applications - 27th Australasian Database Conference, ADC 2016, Proceedings
EditorsMuhammad Aamir Cheema, Wenjie Zhang, Lijun Chang
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319469218
Publication statusPublished - 2016
Externally publishedYes
Event27th Australasian Database Conference on Databases Theory and Applications, ADC 2016 - Sydney, United States
Duration: 28 Sept 201629 Sept 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9877 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference27th Australasian Database Conference on Databases Theory and Applications, ADC 2016
Country/TerritoryUnited States


  • Face recognition
  • Sparse representation
  • Virtual image

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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