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Optimizing Distributed Face Recognition Systems through Efficient Aggregation of Facial Embeddings

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Název: Optimizing Distributed Face Recognition Systems through Efficient Aggregation of Facial Embeddings
Autoři: Philipp Hofer, Michael Roland, René Mayrhofer, Philipp Schwarz
Zdroj: Advances in Artificial Intelligence and Machine Learning. :693-711
Informace o vydavateli: Advances in Artificial Intelligence and Machine Learning, 2023.
Rok vydání: 2023
Popis: 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.
Druh dokumentu: Article
ISSN: 2582-9793
DOI: 10.54364/aaiml.2023.1146
Přístupové číslo: edsair.doi...........ffeb684cf956f3a50fdcd1ec39be8611
Databáze: OpenAIRE
Popis
Abstrakt: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.
ISSN:25829793
DOI:10.54364/aaiml.2023.1146