BehavePassDB: Public Database for Mobile Behavioral Biometrics and Benchmark Evaluation
This work addresses the need for standardized evaluation in mobile behavioral biometrics for authentication, though it is incremental as it builds on existing methods with a new database.
The authors tackled the problem of distinguishing between user and device in mobile behavioral biometrics by introducing BehavePassDB, a public database with structured acquisition sessions and tasks, and achieved competitive results using an LSTM-based system with triplet loss and modality fusion.
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.