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Authors: Philipp Hofer 1 ; Michael Roland 1 ; Philipp Schwarz 2 and René Mayrhofer 1

Affiliations: 1 Johannes Kepler University Linz, Institute of Networks and Security, Austria ; 2 Johannes Kepler University Linz, LIT Secure and Correct Systems Lab, Austria

Keyword(s): Biometric Authentication, Face Embedding, Face Recognition, Aggregation, Decentralization.

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.

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Paper citation in several formats:
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 - ICISSP; ISBN 978-989-758-624-8; ISSN 2184-4356, SciTePress, pages 279-286. DOI: 10.5220/0011599300003405

@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 - ICISSP},
year={2023},
pages={279-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011599300003405},
isbn={978-989-758-624-8},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Information Systems Security and Privacy - ICISSP
TI - Efficient Aggregation of Face Embeddings for Decentralized Face Recognition Deployments
SN - 978-989-758-624-8
IS - 2184-4356
AU - Hofer, P.
AU - Roland, M.
AU - Schwarz, P.
AU - Mayrhofer, R.
PY - 2023
SP - 279
EP - 286
DO - 10.5220/0011599300003405
PB - SciTePress

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