Data di Pubblicazione:
2023
Citazione:
MultiCardioNet: Interoperability between ECG and PPG biometrics / R. Donida Labati, V. Piuri, F. Rundo, F. Scotti. - In: PATTERN RECOGNITION LETTERS. - ISSN 0167-8655. - 175:(2023), pp. 1-7. [10.1016/j.patrec.2023.09.009]
Abstract:
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 con-
tinuous/periodic recognition. Most studies on heart biometrics concern electrocardiogram (ECG) and photo-
plethysmogram (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.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Biometrics; ECG; Interoperability; PPG; Siamese networks
Elenco autori:
R. Donida Labati, V. Piuri, F. Rundo, F. Scotti
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