Face recognition using FLDA with single training image per person

Quan xue Gao, Lei Zhang, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

163 Citations (Scopus)


Fisher linear discriminant analysis (FLDA) has been widely used for feature extraction in face recognition. However, it cannot be used when each object has only one training sample because the intra-class variations cannot be statistically measured in this case. In this paper, a novel method is proposed to solve this problem by evaluating the within-class scatter matrix from the available single training image. By using singular value decomposition (SVD), we decompose the face image into two complementary parts: a smooth general appearance image and a difference image. The later is used to approximately evaluate the within-class scatter matrix and thus the FLDA can be applied to extract the discriminant face features. Experimental results show that the proposed method is efficient and it can achieve higher recognition accuracy than many existing schemes.
Original languageEnglish
Pages (from-to)726-734
Number of pages9
JournalApplied Mathematics and Computation
Issue number2
Publication statusPublished - 15 Nov 2008


  • Face recognition
  • Fisher linear discriminant analysis (FLDA)
  • Single training image per person
  • Singular value decomposition (SVD)

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics


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