SigD: A Cross-Session Dataset for PPG-based User Authentication in Different Demographic Groups.

Recently, unobservable physiological signals have received widespread attention from researchers as unique identifiers of users in biometrics. However, due to the lack of data sets, existing methods are limited in evaluating cross-session scenarios. Cross-session means that signals are collected at different sessions (times). In real scenarios, authentication is almost always cross-session. Currently, the datasets commonly used for Photoplethysmogram (PPG) signal authentication span around one month, which is insufficient for authentication. On the other hand, different demographic groups have different hemodynamic characteristics, but existing methods lack an assessment of these aspects. This paper introduces a dataset to provide insights into PPG signal-based authentication across different time spans and user groups (age, gender). As physiological signals offer unique advantages for user authentication, the potential of PPG signals is gradually explored. Furthermore, our comparative analysis of recent publications on data-driven user authentication using PPG can further identify the similarities and differences among the performance of the proposed authentication models. Our findings may help future research towards a consensus on an appropriate set of performance metrics.