ISSN :2582-9793

Optimizing Distributed Face Recognition Systems through Efficient Aggregation of Facial Embeddings

Original Research (Published On: 23-Feb-2023 )
Optimizing Distributed Face Recognition Systems through Efficient Aggregation of Facial Embeddings
DOI : 10.54364/AAIML.2023.1146

Philipp Hofer, Michael Roland, Philipp Schwarz and René Mayrhofer

Adv. Artif. Intell. Mach. Learn., 3 (1):693-711

Philipp Hofer : Johannes Kepler University Linz, Institute of Networks and Security

Michael Roland : Johannes Kepler University Linz, Institute of Networks and Security

Philipp Schwarz : Johannes Kepler University Linz, LIT Secure and Correct Systems Lab

René Mayrhofer : Johannes Kepler University Linz, Institute of Networks and Security

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DOI: 10.54364/AAIML.2023.1146

Article History: Received on: 20-Jan-23, Accepted on: 06-Feb-23, Published on: 23-Feb-23

Corresponding Author: Philipp Hofer

Email: philipp.hofer@ins.jku.at

Citation: Philipp Hofer (2023). Optimizing Distributed Face Recognition Systems through Efficient Aggregation of Facial Embeddings. Adv. Artif. Intell. Mach. Learn., 3 (1 ):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.

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