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Pré-Publication, Document De Travail Année : 2023

Deep Features Fusion for User Authentication Based on Human Activity

Résumé

The exponential growth in the use of smartphones means that users must constantly be concerned about the security and privacy of mobile data because the loss of a mobile device could compromise personal information. To address this issue, continuous authentication systems have been proposed, in which users are monitored transparently after initial access to the smartphone. In this paper, we address the problem of user authentication by considering human activities as behavioral biometric information. We convert the behavioral biometric data (considered as time series) into a 2D color image. This transformation process keeps all the characteristics of the behavioral signal. Time series does not receive any filtering operation with this transformation and the method is reversible. This signal-to-image transformation allows us to use the 2D convolutional networks to build efficient deep feature vectors. This allows us to compare these feature vectors to the reference template vectors to compute the performance metric. We evaluate the performance of the authentication system in terms of Equal Error Rate (EER) on a benchmark UCI-HAR dataset and we show the efficiency of the approach.
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Dates et versions

hal-04133255 , version 1 (19-06-2023)

Identifiants

  • HAL Id : hal-04133255 , version 1

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Yris Brice Wandji Piugie, Christophe Charrier, Joël Di Manno, Christophe Rosenberger. Deep Features Fusion for User Authentication Based on Human Activity. 2023. ⟨hal-04133255⟩
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