Improving Identification Performance by Integrating Evidence from Sequences

Edwards, Gareth J.; Taylor, Christopher J.; Cootes, Timothy F.

doi:10.1109/CVPR.1999.786982

Improving Identification Performance by Integrating Evidence from Sequences

Gareth J. Edwards, Christopher J. Taylor, Timothy F. Cootes

CVPR 1999 pp. 1486-1491

doi:10.1109/CVPR.1999.786982 /cvpr/1999/edwards1999cvpr-improving/

Abstract

We present a quantitative evaluation of an algorithm for model-based face recognition. The algorithm actively learns how individual faces vary through video sequences, providing on-line suppression of confounding factors such as expression, lighting and pose. By actively decoupling sources of image variation, the algorithm provides a framework in which identity evidence can be integrated over a sequence. We demonstrate that face recognition can be considerably improved by the analysis of video sequences. The method presented is widely applicable in many multi-class interpretation problems.

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Cite

Text

Edwards et al. "Improving Identification Performance by Integrating Evidence from Sequences." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999. doi:10.1109/CVPR.1999.786982

Markdown

[Edwards et al. "Improving Identification Performance by Integrating Evidence from Sequences." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999.](https://mlanthology.org/cvpr/1999/edwards1999cvpr-improving/) doi:10.1109/CVPR.1999.786982

BibTeX

@inproceedings{edwards1999cvpr-improving,
  title     = {{Improving Identification Performance by Integrating Evidence from Sequences}},
  author    = {Edwards, Gareth J. and Taylor, Christopher J. and Cootes, Timothy F.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1999},
  pages     = {1486-1491},
  doi       = {10.1109/CVPR.1999.786982},
  url       = {https://mlanthology.org/cvpr/1999/edwards1999cvpr-improving/}
}