Reconstruction and analysis of multi-pose face images based on nonlinear dimensionality reduction

Changshui Zhang, Jun Wang, Nanyuan Zhao, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

55 Citations (Scopus)

Abstract

Locally linear embedding (LLE) is a nonlinear dimensionality reduction method proposed recently. It can reveal the intrinsic distribution of data, which cannot be provided by classical linear dimensionality reduction methods. The application of LLE, however, is limited because of its lack of a parametric mapping between the observation and the low-dimensional output. And the large data set to be reduced is necessary. In this paper, we propose methods to establish the process of mapping from low-dimensional embedded space to high-dimensional space for LLE and validate their efficiency with the application of reconstruction of multi-pose face images. Furthermore, we propose that the high-dimensional structure of multi-pose face images is similar for the same kind of pose change mode of different persons. So given the structure information of data distribution which is obtained by leaning large numbers of multi-pose images in a training set, the support vector regression (SVR) method of statistical learning theory is used to learn the high-dimensional structure of someone based on small sets. The detailed learning method and algorithm are given and applied to reconstruct and synthesize face images in small set cases. The experiments prove that our idea and method is correct.
Original languageEnglish
Pages (from-to)325-336
Number of pages12
JournalPattern Recognition
Volume37
Issue number2
DOIs
Publication statusPublished - 1 Jan 2004
Externally publishedYes

Keywords

  • Face recognition
  • Locally linear embedding
  • Nonlinear dimensionality reduction
  • Support vector regression

ASJC Scopus subject areas

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

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