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Predicting Face Recognition Performance in Unconstrained Environments
AbstractWhile 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.
Related Material
[pdf][bibtex]@InProceedings{Phillips_2017_CVPR_Workshops,
author = {Jonathon Phillips, P. and Yates, Amy N. and Ross Beveridge, J. and Givens, Geof},
title = {Predicting Face Recognition Performance in Unconstrained Environments},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}
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