ML Anthology

A Comprehensive Empirical Study on Linear Subspace Methods for Facial Expression Analysis

Caifeng Shan, Shaogang Gong, Peter W. McOwan
CVPRW 2006 pp. 153
doi:10.1109/CVPRW.2006.13 /cvprw/2006/shan2006cvprw-comprehensive/

Abstract

Automatic facial expression analysis is a vital component of intelligent Human-Computer Interaction (HCI). In this paper, we present a extensive empirical study on linear subspace methods for facial expression analysis. Locality Preserving Projections (LPP) and Orthogonal Neighborhood Preserving Projections (ONPP) are first time applied to facial expression analysis. We systematically examine a number of linear subspace methods, and show that, in our comparative studies, the Supervised LPP (SLPP) is superior in supervised methods, while ONPP performs best in unsupervised learning.

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Text

Shan et al. "A Comprehensive Empirical Study on Linear Subspace Methods for Facial Expression Analysis." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006. doi:10.1109/CVPRW.2006.13

Markdown

[Shan et al. "A Comprehensive Empirical Study on Linear Subspace Methods for Facial Expression Analysis." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006.](https://mlanthology.org/cvprw/2006/shan2006cvprw-comprehensive/) doi:10.1109/CVPRW.2006.13

BibTeX

@inproceedings{shan2006cvprw-comprehensive,
  title     = {{A Comprehensive Empirical Study on Linear Subspace Methods for Facial Expression Analysis}},
  author    = {Shan, Caifeng and Gong, Shaogang and McOwan, Peter W.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2006},
  pages     = {153},
  doi       = {10.1109/CVPRW.2006.13},
  url       = {https://mlanthology.org/cvprw/2006/shan2006cvprw-comprehensive/}
}