  In the first half of the 20th century, a first pulse oximeter was available
to measure blood flow changes in the peripheral vascular net. However, it was
not until recent times the PhotoPlethysmoGraphic (PPG) signal used to monitor
many physiological parameters in clinical environments. Over the last decade,
its use has extended to the area of biometrics, with different methods that
allow the extraction of characteristic features of each individual from the PPG
signal morphology, highly varying with time and the physical states of the
subject. In this paper, we present a novel PPG-based biometric authentication
system based on convolutional neural networks. Contrary to previous approaches,
our method extracts the PPG signal's biometric characteristics from its
diffusive dynamics, characterized by geometric patterns image in the (p,
q)-planes specific to the 0-1 test. The diffusive dynamics of the PPG signal
are strongly dependent on the vascular bed's biostructure, which is unique to
each individual, and highly stable over time and other psychosomatic
conditions. Besides its robustness, our biometric method is anti-spoofing,
given the convoluted nature of the blood network. Our biometric authentication
system reaches very low Equal Error Rates (ERRs) with a single attempt, making
it possible, by the very nature of the envisaged solution, to implement it in
miniature components easily integrated into wearable biometric systems.
