A Framework of 2D Fisher Discriminant Analysis: Application to Face Recognition with Small Number of Training Samples

Hui Kong, Lei Wang, Eam Khwang Teoh, Jian-Gang Wang, Ronda Venkateswarlu

CVPR 2005 pp. 1083-1088

doi:10.1109/CVPR.2005.30 /cvpr/2005/kong2005cvpr-framework/

Abstract

A novel framework called 2D Fisher discriminant analysis (2D-FDA) is proposed to deal with the small sample size (SSS) problem in conventional one-dimensional linear discriminant analysis (1D-LDA). Different from the 1D-LDA based approaches, 2D-FDA is based on 2D image matrices rather than column vectors so the image matrix does not need to be transformed into a long vector before feature extraction. The advantage arising in this way is that the SSS problem does not exist any more because the between-class and within-class scatter matrices constructed in 2D-FDA are both of full-rank. This framework contains unilateral and bilateral 2D-FDA. It is applied to face recognition where only few training images exist for each subject. Both the unilateral and bilateral 2D-FDA achieve excellent performance on two public databases: ORL database and Yale face database B.

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Cite

Text

Kong et al. "A Framework of 2D Fisher Discriminant Analysis: Application to Face Recognition with Small Number of Training Samples." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.30

Markdown

[Kong et al. "A Framework of 2D Fisher Discriminant Analysis: Application to Face Recognition with Small Number of Training Samples." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/kong2005cvpr-framework/) doi:10.1109/CVPR.2005.30

BibTeX

@inproceedings{kong2005cvpr-framework,
  title     = {{A Framework of 2D Fisher Discriminant Analysis: Application to Face Recognition with Small Number of Training Samples}},
  author    = {Kong, Hui and Wang, Lei and Teoh, Eam Khwang and Wang, Jian-Gang and Venkateswarlu, Ronda},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2005},
  pages     = {1083-1088},
  doi       = {10.1109/CVPR.2005.30},
  url       = {https://mlanthology.org/cvpr/2005/kong2005cvpr-framework/}
}