Efficient Aggregation of Face Embeddings for Decentralized Face Recognition Deployments

Philipp Hofer, Michael Roland, Philipp Schwarz, René Mayrhofer

2023

Abstract

Ubiquitous authentication systems with a focus on privacy favor decentralized approaches as they reduce potential attack vectors, both on a technical and organizational level. The gold standard is to let the user be in control of where their own data is stored, which consequently leads to a high variety of devices used what in turn often incurs additional network overhead. Therefore, when using face recognition, an efficient way to compare faces is important in practical deployments. This paper proposes an efficient way to aggregate embeddings used for face recognition based on an extensive analysis on different datasets and the use of different aggregation strategies. As part of this analysis, a new dataset has been collected, which is available for research purposes. Our proposed method supports the construction of massively scalable, decentralized face recognition systems with a focus on both privacy and long-term usability.

Paper Citation

in Harvard Style

Hofer P., Roland M., Schwarz P. and Mayrhofer R. (2023). Efficient Aggregation of Face Embeddings for Decentralized Face Recognition Deployments. In Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-624-8, pages 279-286. DOI: 10.5220/0011599300003405

in Bibtex Style

@conference{icissp23,
author={Philipp Hofer and Michael Roland and Philipp Schwarz and René Mayrhofer},
title={Efficient Aggregation of Face Embeddings for Decentralized Face Recognition Deployments},
booktitle={Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2023},
pages={279-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011599300003405},
isbn={978-989-758-624-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Efficient Aggregation of Face Embeddings for Decentralized Face Recognition Deployments
SN - 978-989-758-624-8
AU - Hofer P.
AU - Roland M.
AU - Schwarz P.
AU - Mayrhofer R.
PY - 2023
SP - 279
EP - 286
DO - 10.5220/0011599300003405