Optimal subset-division based discrimination and its kernelization for face and palmprint recognition

Xiaoyuan Jing, Sheng Li, Dapeng Zhang, Chao Lan, Jingyu Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

20 Citations (Scopus)


Discriminant analysis is effective in extracting discriminative features and reducing dimensionality. In this paper, we propose an optimal subset-division based discrimination (OSDD) approach to enhance the classification performance of discriminant analysis technique. OSDD first divides the sample set into several subsets by using an improved stability criterion and K-means algorithm. We separately calculate the optimal discriminant vectors from each subset. Then we construct the projection transformation by combining the discriminant vectors derived from all subsets. Furthermore, we provide a nonlinear extension of OSDD, that is, the optimal subset-division based kernel discrimination (OSKD) approach. It employs the kernel K-means algorithm to divide the sample set in the kernel space and obtains the nonlinear projection transformation. The proposed approaches are applied to face and palmprint recognition, and are examined using the AR and FERET face databases and the PolyU palmprint database. The experimental results demonstrate that the proposed approaches outperform several related linear and nonlinear discriminant analysis methods.
Original languageEnglish
Pages (from-to)3590-3602
Number of pages13
JournalPattern Recognition
Issue number10
Publication statusPublished - 1 Oct 2012


  • Discriminant analysis
  • Face recognition
  • Improved stability criterion
  • Kernel method
  • Optimal subset-division
  • Palmprint recognition

ASJC Scopus subject areas

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

Cite this