ML Anthology

Generalized Quotient Image

Haitao Wang, Stan Z. Li, Yangsheng Wang
CVPR 2004 pp. 498-505
doi:10.1109/CVPR.2004.114 /cvpr/2004/wang2004cvpr-generalized/

Abstract

We present a unified framework for modeling intrinsic properties of face images for recognition. It is based on the quotient image (QI) concept, in particular on the existing works of QI, spherical harmonic, image ratio and retinex. Under this framework, we generalize these previous works into two new algorithms: (1) non-point light quotient image (NPL-QI) extends QI to deal with non-point light sources by modeling non-point light directions using spherical harmonic bases; (2) self-quotient image (S-QI) extends QI to perform illumination subtraction without the need for alignment and no shadow assumption. Experimental results show that our algorithms can significantly improve the performance of face recognition under varying illumination conditions.

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Cite

Text

Wang et al. "Generalized Quotient Image." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004. doi:10.1109/CVPR.2004.114

Markdown

[Wang et al. "Generalized Quotient Image." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004.](https://mlanthology.org/cvpr/2004/wang2004cvpr-generalized/) doi:10.1109/CVPR.2004.114

BibTeX

@inproceedings{wang2004cvpr-generalized,
  title     = {{Generalized Quotient Image}},
  author    = {Wang, Haitao and Li, Stan Z. and Wang, Yangsheng},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2004},
  pages     = {498-505},
  doi       = {10.1109/CVPR.2004.114},
  url       = {https://mlanthology.org/cvpr/2004/wang2004cvpr-generalized/}
}