Efficient aggregation of face embeddings for decentralized face recognition deployments (extended version)

TL;DR

This paper introduces an efficient embedding aggregation method for decentralized face recognition, enhancing privacy and scalability while reducing network and hardware demands, supported by extensive analysis and a new dataset.

Contribution

It proposes a novel embedding aggregation strategy tailored for decentralized face recognition, backed by comprehensive dataset analysis and a new publicly available dataset.

Findings

The proposed method improves scalability in decentralized systems.

Analysis shows reduced network overhead with the new aggregation.

The new dataset supports future research in privacy-preserving face recognition.

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.