MultiCardioNet: Interoperability between ECG and PPG biometrics

Compared to other well-known biometric technologies based on physiological traits (e.g., fingerprint, iris, and face), heart biometrics are more robust to presentation attacks and are particularly suitable for continuous/periodic recognition. Most studies on heart biometrics concern electrocardiogram (ECG) and photoplethysmogram (PPG). While the reported results are encouraging, to the best of our knowledge, no studies have been conducted on the interoperability between ECG and PPG biometrics. We present a novel method that is capable of performing single-domain and multiple-domain identity verifications for ECG and PPG signals, providing interoperability between the heterogeneous cardiac signals. Our method does not require the computation of any reference/fiducial point and uses a compact representation of the given signals. We propose MultiCardioNet, a novel Siamese neural network trained by using an ad hoc learning algorithm. MultiCardioNet computes a similarity score between two spectrogram-based representations of cardiac signals. Our learning algorithm iteratively computes a balanced subset of genuine and impostor pairs during the training epochs. We performed experiments on a dataset containing 1,008 pairs of ECG and PPG samples, obtaining accuracy comparable to that of the state-of-the-art methods for single-domain scenarios and demonstrating only a relatively small performance decrease in the multiple-domain scenario.