Mobile Passive Authentication through Touchscreen and Background Sensor Data

Stragapede, Giuseppe; Vera-Rodríguez, Rubén; Tolosana, Rubén; Morales, Aythami; Acien, Alejandro; Lan, Gaël Le

doi:10.1109/iwbf55382.2022.9794524

Cited by 3 publications

(1 citation statement)

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“…In [7], [23], several experiments are performed over Hu-MIdb, a separate RNN with triplet loss is implemented for each single modality with the weighted fusion of the different modalities is carried out at score level, leading to EER ranging from 4% to 9% depending on the modality combination in a 3-second interval.…”

Section: A Bbca Systemsmentioning

confidence: 99%

BehavePassDB: Public Database for Mobile Behavioral Biometrics and Benchmark Evaluation

Stragapede¹,

Vera-Rodríguez²,

Tolosana³

et al. 2022

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Mobile behavioral biometrics have become a popular topic of research, reaching promising results in terms of authentication, exploiting a multimodal combination of touchscreen and background sensor data. However, there is no way of knowing whether state-of-the-art classifiers in the literature can distinguish between the notion of user and device. In this article, we present a new database, BehavePassDB, structured into separate acquisition sessions and tasks to mimic the most common aspects of mobile Human-Computer Interaction (HCI). BehavePassDB is acquired through a dedicated mobile app installed on the subjects' devices, also including the case of different users on the same device for evaluation. We propose a standard experimental protocol and benchmark for the research community to perform a fair comparison of novel approaches with the state of the art 1 . We propose and evaluate a system based on Long-Short Term Memory (LSTM) architecture with triplet loss and modality fusion at score level.

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Section: A Bbca Systemsmentioning

confidence: 99%