Human face analysis with nonlinear manifold learning

Xiao Kan Wang, Xia Mao*, Ishizuka Mitsuru

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Since human face movements distribute on a nonlinear manifold, there are inherent alignment residuals brought by the global linearity hypothesis in the traditional Principal Component Analysis (PCA) based Active Appearance Models (AAM). In this paper, a famous manifold learning method, Local Linear Embedding (LLE) is improved to model human face shape space for reducing the inherent alignment residuals. The experimental results show that the method, LLE-AAM, obtains lower alignment residuals to the tiny alterations of human face and still make successful alignment when PCA-AAM failed to some large alterations. According to the statistical analysis, LLE-AAM could reduce the residual to a certain extent.

Original languageEnglish
Pages (from-to)2531-2535
Number of pages5
JournalDianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology
Issue number10
Publication statusPublished - 2011 Oct
Externally publishedYes


  • Active Appearance Models (AAM)
  • Face recognition
  • Local Linear Embedding (LLE)
  • Nonlinear manifold learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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