END-to-END Photopleth YsmographY (PPG) Based Biometric Authentication by Using Convolutional Neural Networks

Whilst research efforts have traditionally focused on Electrocardiographic (ECG) signals and handcrafted features as potential biometric traits, few works have explored systems based on the raw pho-toplethysmogram (PPG) signal. This work proposes an end-to-end architecture to offer biometric authentication using PPG biosensors through Convolutional Networks. We provide an evaluation of the performance of our approach in two different databases: Troika and PulseID, the latter a publicly available database specifically collected by the authors for such a purpose. Our verification approach through convolutional network based models and using raw PPG signals appears to be viable in current monitoring procedures within e-health and fitness environments showing a remarkable potential as a biometry. The approach tested on a verification fashion, on trials lasting one second, achieved an AUC of 78.2% and 83.2%, averaged among target subjects, on PulseID and Troika datasets respectively. Our experimental results on previous small datasets support the usefulness of PPG extracted biomarkers as viable traits for multi-biometric or standalone biometrics. Furthermore, the approach results in a low input throughput and complexity that allows for a continuous authentication in real-world scenarios. Nevertheless, the reported experiments also suggest that further research is necessary to account for and understand sources of variability found in some subjects.