Single-sample face recognition based on intra-class differences in a variation model

Jun Cai, Jing Chen*, Xing Liang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

In this paper, a novel random facial variation modeling system for sparse representation face recognition is presented. Although recently Sparse Representation-Based Classification (SRC) has represented a breakthrough in the field of face recognition due to its good performance and robustness, there is the critical problem that SRC needs sufficiently large training samples to achieve good performance. To address these issues, we challenge the single-sample face recognition problem with intra-class differences of variation in a facial image model based on random projection and sparse representation. In this paper, we present a developed facial variation modeling systems composed only of various facial variations. We further propose a novel facial random noise dictionary learning method that is invariant to different faces. The experiment results on the AR, Yale B, Extended Yale B, MIT and FEI databases validate that our method leads to substantial improvements, particularly in single-sample face recognition problems.

Original languageEnglish
Pages (from-to)1071-1087
Number of pages17
JournalSensors
Volume15
Issue number1
DOIs
Publication statusPublished - 8 Jan 2015

Keywords

  • Face recognition
  • Intra-class variation model differences
  • Sparse representation

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