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Linear Subspaces for Illumination Robust Face Recognition

Aziz Umit Batur, Monson H. Hayes Iii
CVPR 2001 pp. II:296-301
doi:10.1109/CVPR.2001.990974 /cvpr/2001/batur2001cvpr-linear/

Abstract

In this paper, we present a segmented linear subspace model for face recognition that is robust under varying illumination conditions. The algorithm generalizes the 3D illumination subspace model by segmenting the image into regions that have surface normals whose directions are close to each other. This segmentation is performed using a K-means clustering algorithm and requires only a few training images under different illuminations. When the linear subspace model is applied to the segmented image, recognition is robust to attached and cast shadows, and the recognition rate is equal to that of computationally more complex systems that require constructing the 3D surface of the face.

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Cite

Text

Batur and Iii. "Linear Subspaces for Illumination Robust Face Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990974

Markdown

[Batur and Iii. "Linear Subspaces for Illumination Robust Face Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/batur2001cvpr-linear/) doi:10.1109/CVPR.2001.990974

BibTeX

@inproceedings{batur2001cvpr-linear,
  title     = {{Linear Subspaces for Illumination Robust Face Recognition}},
  author    = {Batur, Aziz Umit and Iii, Monson H. Hayes},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2001},
  pages     = {II:296-301},
  doi       = {10.1109/CVPR.2001.990974},
  url       = {https://mlanthology.org/cvpr/2001/batur2001cvpr-linear/}
}