Linear collaborative discriminant regression classification for face recognition

Xiaochao Qu, Suah Kim, Run Cui, Hyong Joong Kim

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

This paper proposes a novel face recognition method that improves Huang's linear discriminant regression classification (LDRC) algorithm. The original work finds a discriminant subspace by maximizing the between-class reconstruction error and minimizing the within-class reconstruction error simultaneously, where the reconstruction error is obtained using Linear Regression Classification (LRC). However, the maximization of the overall between-class reconstruction error is easily dominated by some large class-specific between-class reconstruction errors, which makes the following LRC erroneous. This paper adopts a better between-class reconstruction error measurement which is obtained using the collaborative representation instead of class-specific representation and can be regarded as the lower bound of all the class-specific between-class reconstruction errors. Therefore, the maximization of the collaborative between-class reconstruction error maximizes each class-specific between-class reconstruction and emphasizes the small class-specific between-class reconstruction errors, which is beneficial for the following LRC. Extensive experiments are conducted and the effectiveness of the proposed method is verified.

Original languageEnglish
Pages (from-to)312-319
Number of pages8
JournalJournal of Visual Communication and Image Representation
Volume31
DOIs
Publication statusPublished - 2015 Jul 27

Keywords

  • Collaborative representation
  • Dimensionality reduction
  • Face recognition
  • Feature extraction
  • Linear collaborative discriminant regression classification
  • Linear discriminant regression classification
  • Linear regression classification
  • Sparse representation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Signal Processing

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