Structured regularized robust coding for face recognition

Meng Yang, Tiancheng Song, Feng Liu, Linlin Shen

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

4 Citations (Scopus)


The sparse representation based classifier (SRC) has been successfully applied to robust face recognition (FR) with various variations. To achieve much stronger robustness to facial occlusion, recently regularized robust coding (RRC) was proposed by designing a new robust representation residual term. Although RRC has achieved the leading performance, it ignores the structured information (i.e., spatial consistence) embedded in the occluded pixels. In this paper, we proposed a novel structured regularized robust coding (SRRC) framework, in which the spatial consistence of occluded pixels was exploited by pixel weight learning (PWL) model. Efficient algorithms were also proposed to fastly learn the pixel’s weight and accurately recover the occluded area. The experiments on face recognition in several representative datasets clearly show the advantage of the proposed SRRC in accuracy and efficiency.

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

Publication series

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


Conference1st Chinese Conference on Computer Vision, CCCV 2015


  • Face recognition
  • Robust coding
  • Structure regularized

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


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