Using the idea of the sparse representation to perform coarse-to-fine face recognition

Yong Xu, Qi Zhu, Zizhu Fan, Dapeng Zhang, Jianxun Mi, Zhihui Lai

Research output: Journal article publicationJournal articleAcademic researchpeer-review

137 Citations (Scopus)


In this paper, we propose a coarse-to-fine face recognition method. This method consists of two stages and works in a similar way as the well-known sparse representation method. The first stage determines a linear combination of all the training samples that is approximately equal to the test sample. This stage exploits the determined linear combination to coarsely determine candidate class labels of the test sample. The second stage again determines a weighted sum of all the training samples from the candidate classes that is approximately equal to the test sample and uses the weighted sum to perform classification. The rationale of the proposed method is as follows: the first stage identifies the classes that are "far" from the test sample and removes them from the set of the training samples. Then the method will assign the test sample into one of the remaining classes and the classification problem becomes a simpler one with fewer classes. The proposed method not only has a high accuracy but also can be clearly interpreted.
Original languageEnglish
Pages (from-to)138-148
Number of pages11
JournalInformation Sciences
Publication statusPublished - 20 Jul 2013


  • Biometrics
  • Decision making
  • Face recognition
  • Information fusion
  • Security access

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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