  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. We propose and evaluate a system based on
Long-Short Term Memory (LSTM) architecture with triplet loss and modality
fusion at score level.
