ML Anthology

Kernel Fukunaga-Koontz Transform Subspaces for Enhanced Face Recognition

Yung-hui Li, Marios Savvides
CVPR 2007
doi:10.1109/CVPR.2007.383398 /cvpr/2007/li2007cvpr-kernel/

Abstract

Traditional linear Fukunaga-Koontz transform (FKT) (F. Fukunaga and W. Koontz, 1970) is a powerful discriminative subspaces building approach. Previous work has successfully extended FKT to be able to deal with small-sample-size. In this paper, we extend traditional linear FKT to enable it to work in multi-class problem and also in higher dimensional (kernel) subspaces and therefore provide enhanced discrimination ability. We verify the effectiveness of the proposed kernel Fukunaga-Koontz transform by demonstrating its effectiveness in face recognition applications; however the proposed non-linear generalization can be applied to any other domain specific problems.

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Cite

Text

Li and Savvides. "Kernel Fukunaga-Koontz Transform Subspaces for Enhanced Face Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383398

Markdown

[Li and Savvides. "Kernel Fukunaga-Koontz Transform Subspaces for Enhanced Face Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/li2007cvpr-kernel/) doi:10.1109/CVPR.2007.383398

BibTeX

@inproceedings{li2007cvpr-kernel,
  title     = {{Kernel Fukunaga-Koontz Transform Subspaces for Enhanced Face Recognition}},
  author    = {Li, Yung-hui and Savvides, Marios},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383398},
  url       = {https://mlanthology.org/cvpr/2007/li2007cvpr-kernel/}
}