In:
Advances in Artificial Intelligence and Machine Learning, Advances in Artificial Intelligence and Machine Learning, Vol. 03, No. 01 ( 2023), p. 693-711
Abstract:
Biometrics are one of the most privacy-sensitive data. 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. Moreover, in comparison with a centralized system, designs with higher end-user freedom often incur additional network overhead. Therefore, when using face recognition for biometric authentication, an efficient way to compare faces is important in practical deployments, because it reduces both network and hardware requirements that are essential to encourage device diversity. 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.
Type of Medium:
Online Resource
ISSN:
2582-9793
DOI:
10.54364/AAIML.2023.1146
Language:
Unknown
Publisher:
Advances in Artificial Intelligence and Machine Learning
Publication Date:
2023
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