Discriminant subclass-center manifold preserving projection for face feature extraction

Chao Lan, Xiaoyuan Jing, Dapeng Zhang, Shiqiang Gao, Jingyu Yang

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

6 Citations (Scopus)


Manifold learning is an effective feature extraction technique, which seeks a low-dimensional space where the manifold structure, in terms of local neighborhood, of the data set can be well preserved. A typical manifold learning method constructs a local neighborhood centered at individual samples. In this paper, we propose to construct local neighborhoods that centered at subclass centers, and seek an embedded space where such neighborhood is well preserved. We show from a probability perspective that, neighbors of a subclass center would contain more intra-class data than inter-class data, which may be desirable for discrimination. Meanwhile, we simultaneously enhance the discriminative power of extracted features by maximizing the Fisher ratio of embedded data based on subclass centers. Experimental results on CAS-PEAL and FERET face databases demonstrate that our proposed approach is more effective than most typical manifold learning methods and their supervised extensions in classification performance.
Original languageEnglish
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Number of pages4
Publication statusPublished - 1 Dec 2011
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: 11 Sep 201114 Sep 2011


Conference2011 18th IEEE International Conference on Image Processing, ICIP 2011


  • Discriminant subclass-center manifold preserving projection (DSMPP)
  • Face feature extraction
  • Manifold learning
  • Subclass-center neighborhood structure

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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