Abstract
This paper presents the first photoplethysmographic (PPG) signal dynamic-based biometric authentication system with a Siamese convolutional neural network (CNN). Our method extracts the PPG signal’s biometric characteristics from its diffusive dynamics, characterized by geometric patterns in the -planes specific to the 0–1 test. PPG signal diffusive dynamics are strongly dependent on the vascular bed’s biostructure, unique to each individual. The dynamic characteristics of the PPG signal are more stable over time than its morphological features, particularly in the presence of psychosomatic conditions. Besides its robustness, our biometric method is anti-spoofing, given the complex nature of the blood network. Our proposal trains using a national research study database with 40 real-world PPG signals measured with commercial equipment. 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.
Highlights
The relentless outbreak of the pandemic in our lives has put the globalized world in check
In PPG-based biometrics within the deep neural network (DNN) framework, as a general concept of the system, we propose a biometric system based on the diffusive dynamics of the PPG signal with a DNN design adapted to diffusive images and a specific biometrics method
We show the biometric potential of the diffusive dynamics of the PPG signal
Summary
The relentless outbreak of the pandemic in our lives has put the globalized world in check. The role that technology will play in future societies is unquestionable. This profound metamorphosis carries challenges that digital platforms themselves have to face. One of them is to keep the identities of the users of the different services protected, that is, to avoid identity theft so that the platform can unequivocally verify that a user is who they say they are and not an impostor intruder with clearly fraudulent purposes. Compared to traditional access passwords, the different biometric identification systems are reliable and free the user from memorizing numerous keys [5]. The only access password lies in the user’s anatomical characteristic, supposedly exclusive and non-transferable, whose emulation is extremely problematic even for the most seasoned intruders. Voice, iris, palm, and finger recognition are already a reality that safeguards socioeconomic transactions [6,7,8]
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