ML Anthology

Predicting Face Recognition Performance in Unconstrained Environments

P. Jonathon Phillips, Amy N. Yates, J. Ross Beveridge, Geof H. Givens
CVPRW 2017 pp. 557-565
doi:10.1109/CVPRW.2017.83 /cvprw/2017/phillips2017cvprw-predicting/

Abstract

While face recognition algorithms perform under many different unconstrained conditions, predicting this performance is not possible when a new location is introduced. Analyzing the impostor distribution of the videos of the Point-and-Shoot Challenge (PaSC) as well as its relationship to the genuine match distribution, we present a method for predicting the performance of an algorithm using only unlabeled data for a new location.

CVPRW Semantic Scholar

Cite

Text

Phillips et al. "Predicting Face Recognition Performance in Unconstrained Environments." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017. doi:10.1109/CVPRW.2017.83

Markdown

[Phillips et al. "Predicting Face Recognition Performance in Unconstrained Environments." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017.](https://mlanthology.org/cvprw/2017/phillips2017cvprw-predicting/) doi:10.1109/CVPRW.2017.83

BibTeX

@inproceedings{phillips2017cvprw-predicting,
  title     = {{Predicting Face Recognition Performance in Unconstrained Environments}},
  author    = {Phillips, P. Jonathon and Yates, Amy N. and Beveridge, J. Ross and Givens, Geof H.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2017},
  pages     = {557-565},
  doi       = {10.1109/CVPRW.2017.83},
  url       = {https://mlanthology.org/cvprw/2017/phillips2017cvprw-predicting/}
}