ML Anthology

Comparison of Feature Space Methods for Face Recognition

Chunyan Xie, Marios Savvides, B. V. K. Vijaya Kumar
CVPRW 2006 pp. 46
doi:10.1109/CVPRW.2006.58 /cvprw/2006/xie2006cvprw-comparison/

Abstract

Many feature space methods have been investigated for appearance-based face recognition. In this paper we compare a new feature space face recognition method - the class-dependence feature analysis (CFA) with three other popular methods, namely, the principal component analysis (PCA), the linear discriminant analysis (LDA) and the independent component analysis (ICA), for appearance-based 2-D face recognition. The numerical results on the face recognition grand challenge (FRGC) show that the CFA outperforms the other three method

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Cite

Text

Xie et al. "Comparison of Feature Space Methods for Face Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006. doi:10.1109/CVPRW.2006.58

Markdown

[Xie et al. "Comparison of Feature Space Methods for Face Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006.](https://mlanthology.org/cvprw/2006/xie2006cvprw-comparison/) doi:10.1109/CVPRW.2006.58

BibTeX

@inproceedings{xie2006cvprw-comparison,
  title     = {{Comparison of Feature Space Methods for Face Recognition}},
  author    = {Xie, Chunyan and Savvides, Marios and Kumar, B. V. K. Vijaya},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2006},
  pages     = {46},
  doi       = {10.1109/CVPRW.2006.58},
  url       = {https://mlanthology.org/cvprw/2006/xie2006cvprw-comparison/}
}