Mobile devices such as smartphones and smartwatches are part of our everyday lives, acquiring large amounts of personal information which needs to be properly secured. Among the different authentication techniques, behavioural biometrics has become a very popular method as it allows authentication in a non-intrusive and continuous way. We propose M-GaitFormer, a novel mobile biometric gait verification system based on Transformer architectures. The proposed system outperforms other state-of-the-art approaches based on popular Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This biometric system only considers the accelerometer and gyroscope data acquired by the mobile device.
Curriculum vitæ
Paula Delgado de Santos has a bachelor's degree in Telecommunications Engineering from the Universidad Autonoma de Madrid. She later acquired a master’s degree in Telecommunications Engineering while she was pursuing a scholarship of IBM together with her home university. The previous year she was working at a Swiss University, HEIG-VD, as a Data Scientist. In 2020 she began her PhD studies with a Marie Curie Fellowship within the PriMa (Privacy Matters) EU project, supervised by Professor Richard Guest (University of Kent) and Doctor Ruben Tolosana (Universidad Autonoma de Madrid). She will study the richness of background sensor data elements obtained from mobile devices in a continuous authentication scenario.
We thank our media partner:
