Linear discriminant regression classification for face recognition

Shih Ming Huang, Jar-Ferr Yang

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)


To improve the robustness of the linear regression classification (LRC) algorithm, in this paper, we propose a linear discriminant regression classification (LDRC) algorithm to boost the effectiveness of the LRC for face recognition. We embed the Fisher criterion into the LRC as a novel discriminant regression analysis method. The LDRC attempts to maximize the ratio of the between-class reconstruction error (BCRE) over the within-class reconstruction error (WCRE) to find an optimal projection matrix for the LRC such that the LRC on that subspace can achieve a high discrimination for classification. Then, the projected coefficients are executed by the LRC for face recognition. Extensive experiments carried out on the FERET and AR face databases show that the LDRC performs better than the related regression based algorithms and shows a promising ability for face recognition.

Original languageEnglish
Article number6373697
Pages (from-to)91-94
Number of pages4
JournalIEEE Signal Processing Letters
Issue number1
Publication statusPublished - 2013 Jan 1

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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