Robust face recognition via facial disguise learning

Meng Yang, Linlin Shen

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


The sparse representation based classifier (SRC) has been successfully applied to robust face recognition (FR) with various disguises. Following SRC, recently regularized robust coding (RRC) was proposed for more robustness to facial occlusion by designing a new robust representation residual term. Although RRC has achieved the leading performance, it ignores the prior knowledge embedded in facial disguises. In this paper, we proposed a novel facial disguise learning (FDL) model, in which the unknown occlusion pattern in the query image is learned using a collected disguise mask dictionary. Two learning strategies with an iterative reweighted coding algorithm, independent FDL and joint FDL, were presented in this paper. The experiments on face recognition with disguise clearly show the advantage of the proposed FDL in accuracy and efficiency.

Original languageEnglish
Title of host publicationPattern Recognition - 6th Chinese Conference, CCPR 2014, Proceedings
EditorsShutao Li, Yaonan Wang, Chenglin Liu
PublisherSpringer Verlag
Number of pages10
ISBN (Electronic)9783662456422
Publication statusPublished - 2014
Externally publishedYes
Event6th Chinese Conference on Pattern Recognition, CCPR 2014 - Changsha, China
Duration: 17 Nov 201419 Nov 2014

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference6th Chinese Conference on Pattern Recognition, CCPR 2014


  • Facial disguise learning
  • Regularized robust coding
  • Robust face recognition

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


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