Feature extraction based on Laplacian bidirectional maximum margin criterion

Wankou Yang, Jianguo Wang, Mingwu Ren, Jingyu Yang, Lei Zhang, Guanghai Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

69 Citations (Scopus)


Maximum margin criterion (MMC) based feature extraction is more efficient than linear discriminant analysis (LDA) for calculating the discriminant vectors since it does not need to calculate the inverse within-class scatter matrix. However, MMC ignores the discriminative information within the local structures of samples and the structural information embedding in the images. In this paper, we develop a novel criterion, namely Laplacian bidirectional maximum margin criterion (LBMMC), to address the issue. We formulate the image total Laplacian matrix, image within-class Laplacian matrix and image between-class Laplacian matrix using the sample similar weight that is widely used in machine learning. The proposed LBMMC based feature extraction computes the discriminant vectors by maximizing the difference between image between-class Laplacian matrix and image within-class Laplacian matrix in both row and column directions. Experiments on the FERET and Yale face databases show the effectiveness of the proposed LBMMC based feature extraction method.
Original languageEnglish
Pages (from-to)2327-2334
Number of pages8
JournalPattern Recognition
Issue number11
Publication statusPublished - 1 Nov 2009


  • Bidirectional
  • Face recognition
  • Feature extraction
  • Laplacian
  • MMC

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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