Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition
Marco Huber (Fraunhofer IGD),* Philipp Terhörst (Paderborn University), Florian Kirchbuchner (Fraunhofer Institute for Computer Graphics Research IGD), Naser Damer (Fraunhofer IGD ), Arjan Kuijper (Fraunhofer Institute for Computer Graphics Research IGD and Mathematical and Applied Visual Computing group, TU Darmstadt)
The 33rd British Machine Vision Conference
Abstract
Face Recognition (FR) is increasingly used in critical verification decisions and thus, there is a need for assessing the trustworthiness of such decisions. The confidence of a decision is often based on the overall performance of the model or on the image quality. We propose to propagate model uncertainties to scores and decisions in an effort to increase the transparency of verification decisions. This work presents two contributions. First, we propose an approach to estimate the uncertainty of face comparison scores. Second, we introduce a confidence measure of the system's decision to provide insights into the verification decision. The suitability of the comparison scores uncertainties and the verification decision confidences have been experimentally proven on three face recognition models on two datasets.Video
Citation
@inproceedings{Huber_2022_BMVC,
author = {Marco Huber and Philipp Terhörst and Florian Kirchbuchner and Naser Damer and Arjan Kuijper},
title = {Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0506.pdf}
}
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