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The relentless outbreak of the pandemic in our lives has put the globalized world in check. Biometric system results for input data, raw and preprocessed, are studied and compared with eight primary biometric methods related to PPG, achieving the best equal error rate (ERR) and processing times with a single attempt, among all of them.

Our proposal trains using a national research study database with 40 real-world PPG signals measured with commercial equipment. Besides its robustness, our biometric method is anti-spoofing, given the complex nature of the blood network. The dynamic characteristics of the PPG signal are more stable over time than its morphological features, particularly in the presence of psychosomatic conditions. PPG signal diffusive dynamics are strongly dependent on the vascular bed’s biostructure, unique to each individual.

Our method extracts the PPG signal’s biometric characteristics from its diffusive dynamics, characterized by geometric patterns in the ( p, q )-planes specific to the 0–1 test. This paper presents the first photoplethysmographic (PPG) signal dynamic-based biometric authentication system with a Siamese convolutional neural network (CNN).

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