Robust Biometric Statistical Features within Continuous Blood Pressure Signals

Physiological signals have gained significant attention in the field of biometrics due to their unique characteristics and potential for secure and reliable personal identification. However, despite the extensive exploration of various physiological signals, such as the Electrocardiogram (ECG), Electroencephalogram (EEG), and facial recognition, the potential benefits offered by blood pressure (BP) signals in the context of biometric identification systems have remained largely unexplored. Therefore, the primary objective of this paper is to investigate the feasibility and usefulness of incorporating the unique characteristics of blood pressure signals, in combination with a support vector machine (SVM) classifier, for multi-person identification purposes. Through rigorous experimental analysis, we achieved a promising accuracy rate of 87.3% and an AUC value ranging from 94.8% to 99.66%. These promising results shed light on the untapped usefulness of blood pressure signals as a valuable biometric trait, thereby opening up new avenues for their application in biometric identification systems.