Face recognition using a multi-manifold discriminant analysis method

Wankou Yang, Changyin Sun, Lei Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

9 Citations (Scopus)


In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for face feature extraction and face recognition, which is based on graph embedded learning and under the Fisher discirminant analysis framework. In MMDA, the within-class graph and between-class graph are designed to characterize the within-class compactness and the between-class separability, respectively, seeking for the discriminant matrix that simultaneously maximizing the between-class scatter and minimizing the within-class scatter. In addition, the within-class graph can also represent the sub-manifold information and the between-class graph can also represent the multi-manifold information. The proposed MMDA is examined by using the FERET face database, and the experimental results demonstrate that MMDA works well in feature extraction and lead to good recognition performance.
Original languageEnglish
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Number of pages4
Publication statusPublished - 18 Nov 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: 23 Aug 201026 Aug 2010


Conference2010 20th International Conference on Pattern Recognition, ICPR 2010


  • Face recongition
  • LDA
  • Multi-Manifold learning

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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