| Title: |
MultiCardioNet: Interoperability between ECG and PPG biometrics |
| Authors: |
Donida Labati R.; Piuri V.; Rundo F.; Scotti F. |
| Contributors: |
R. Donida Labati; V. Piuri; F. Rundo; F. Scotti |
| Publisher Information: |
Elsevier |
| Publication Year: |
2023 |
| Collection: |
The University of Milan: Archivio Istituzionale della Ricerca (AIR) |
| Subject Terms: |
Biometric; ECG; Interoperability; PPG; Siamese networks; Settore INF/01 - Informatica; Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/wos/WOS:001088639400001; volume:175; firstpage:1; lastpage:7; numberofpages:7; journal:PATTERN RECOGNITION LETTERS; https://hdl.handle.net/2434/1019724 |
| DOI: |
10.1016/j.patrec.2023.09.009 |
| Availability: |
https://hdl.handle.net/2434/1019724; https://doi.org/10.1016/j.patrec.2023.09.009 |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.7B768D03 |
| Database: |
BASE |