Sparse representation classifier steered discriminative projection with applications to face recognition

Jian Yang, Delin Chu, Lei Zhang, Yong Xu, Jingyu Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

181 Citations (Scopus)


A sparse representation-based classifier (SRC) is developed and shows great potential for real-world face recognition. This paper presents a dimensionality reduction method that fits SRC well. SRC adopts a class reconstruction residual-based decision rule, we use it as a criterion to steer the design of a feature extraction method. The method is thus called the SRC steered discriminative projection (SRC-DP). SRC-DP maximizes the ratio of between-class reconstruction residual to within-class reconstruction residual in the projected space and thus enables SRC to achieve better performance. SRC-DP provides low-dimensional representation of human faces to make the SRC-based face recognition system more efficient. Experiments are done on the AR, the extended Yale B, and PIE face image databases, and results demonstrate the proposed method is more effective than other feature extraction methods based on the SRC.
Original languageEnglish
Article number6479351
Pages (from-to)1023-1035
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number7
Publication statusPublished - 23 May 2013


  • Dimensionality reduction
  • discriminant analysis
  • face recognition
  • feature extraction
  • sparse representation

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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