Interpretable evaluation of physiological signals for biometric identification

Biometric systems using physiological signals have shown high identification accuracy (IA) and low Equal Error Rate (EER). However, existing research largely emphasizes performance metrics alone, overlooking the characteristics of models. In contrast, this work shifts the focus toward a deeper understanding of biometric system behavior. Particularly, how signal features, population size, and sample availability influence performance and reliability. To achieve this, we propose a unified framework to analyze ECG, EEG, and PPG signals. Identification performance was assessed experimentally using machine learning models for interpretability. Further, the framework quantifies the importance of individual features using SHapley Additive exPlanations (SHAP). Additionally, sensitivity analyses are performed to study the effects of varying population size and sample availability. All classifiers using the selected feature sets from feature analysis demonstrated strong performance across various evaluation metrics. IA consistently exceeded 96% on the majority of datasets, demonstrating competitiveness with well-established deep models. They notably achieved a lower EER than all other compared deep learning-based machine learning studies. SHAP analysis revealed that wavelet coefficients, especially from the first and sixth decomposition levels, and systolic features in PPG are the most discriminative. By shifting the focus toward these underexplored dimensions, our work enables more informed decisions in biometric system development and deployment under variable conditions. These insights directly support the application and development of secure and privacy-aware biometric systems suitable for real-world deployment, including medical edge learning environments where resource constraints and data sensitivity are critical.