ML Anthology

Bayesian Face Recognition Using Deformable Intensity Surfaces

Baback Moghaddam, Chahab Nastar, Alex Pentland
CVPR 1996 pp. 638-645
doi:10.1109/CVPR.1996.517140 /cvpr/1996/moghaddam1996cvpr-bayesian/

Abstract

We describe a novel technique for face recognition based on deformable intensity surfaces which incorporates both the shape and texture components of the 2D image. The intensity surface of the facial image is modeled as a deformable 3D mesh in (z, y, I(x, y)) space. Using an efficient technique for matching two surfaces (in terms of the analytic modes of vibration), we obtain a dense correspondence field (or 3D warp) between two images. The probability distributions of two classes of warps are then estimated from training data: interpersonal and extrapersonal variations. These densities are then used in a Bayesian framework for image matching and recognition. Experimental results with facial data from the US Army FERET database demonstrate an increased recognition rate over the previous best methods.

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Cite

Text

Moghaddam et al. "Bayesian Face Recognition Using Deformable Intensity Surfaces." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1996. doi:10.1109/CVPR.1996.517140

Markdown

[Moghaddam et al. "Bayesian Face Recognition Using Deformable Intensity Surfaces." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1996.](https://mlanthology.org/cvpr/1996/moghaddam1996cvpr-bayesian/) doi:10.1109/CVPR.1996.517140

BibTeX

@inproceedings{moghaddam1996cvpr-bayesian,
  title     = {{Bayesian Face Recognition Using Deformable Intensity Surfaces}},
  author    = {Moghaddam, Baback and Nastar, Chahab and Pentland, Alex},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1996},
  pages     = {638-645},
  doi       = {10.1109/CVPR.1996.517140},
  url       = {https://mlanthology.org/cvpr/1996/moghaddam1996cvpr-bayesian/}
}