Beyond Context: Identifying Individuals from Physiological Signals Across Experiments

Abstract

This study evaluates the feasibility of using ECG and EDA signals for biometric identification in diverse VR contexts. Participants were first assessed in a controlled puzzle-based VR game and later in a dynamic exergame, separated by a two-year temporal gap. The proposed CNN model achieved 98.9% accuracy in the controlled environment, confirming the reliability of physiological signals for biometric identification. However, a 24% performance decline was observed in the dynamic exergame setting, highlighting the critical challenge of contextual dependence in biometric systems. Unlike most existing studies, which examine time spans of no more than a week, this work provides new insights into the impact of long-term variability and task-induced changes on identification performance. The findings underscore the importance of addressing contextual and temporal variability to improve the robustness and adaptability of biometric models.