2022/06/06 by Giuseppe Stragapede, Stragapede, Giuseppe, Rubén Vera-Rodríguez +5 · 1 citation
Computer Science · Psychology · #Computer Vision and Pattern Recognition (cs.CV) #Digital Mental Health Interventions #Emotion and Mood Recognition #FOS: Computer and information sciences #User Authentication and Security Systems
paper · pdf · doi:10.48550/arxiv.2206.02502
openalex publication_date 2022/06/06 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28
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.