Pathology

Authentication (law)

Biometrics

Human–computer interaction

Geodesy

World Wide Web

Machine learning

Benchmark (surveying)

Artificial intelligence

Geography

Mobile device

Alternative medicine

Touchscreen

Mobile apps

Modality (human–computer interaction)

Protocol (science)

Computer science

Computer security

Medicine

Pattern Recognition

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 art1. We propose and evaluate a system based on Long-Short Term Memory (LSTM) architecture with triplet loss and modality fusion at score level.

BehavePassDB: Public Database for Mobile Behavioral Biometrics and Benchmark Evaluation