Adaptive ECG biometric recognition : a study on re-enrollment methods for QRS signals
Contributo in Atti di convegno
Data di Pubblicazione:
2014
Citazione:
Adaptive ECG biometric recognition : a study on re-enrollment methods for QRS signals / R. Donida Labati, V. Piuri, R. Sassi, F. Scotti, G. Sforza - In: Computational Intelligence in Biometrics and Identity Management (CIBIM), 2014 IEEE Symposium onPiscataway : IEEE, 2014 Dec. - ISBN 9781479945337. - pp. 30-37 (( convegno CIBIM tenutosi a Orlando nel 2014 [10.1109/CIBIM.2014.7015440].
Abstract:
The diffusion of wearable and mobile devices for the acquisition and analysis of cardiac signals drastically increased the possible applicative scenarios of biometric systems based on electrocardiography (ECG). Moreover, such devices allow for comfortable and unconstrained acquisitions of ECG signals for relevant time spans of tens of hours, thus making these physiological signals particularly attractive biometric traits for continuous authentication applications. In this context, recent studies showed that the QRS complex is the most stable component of the ECG signal, but the accuracy of the authentication degrades over time, due to significant variations in the patterns for each individual. Adaptive techniques for automatic template update can therefore become enabling technologies for continuous authentication systems based on ECG characteristics.
Tipologia IRIS:
03 - Contributo in volume
Keywords:
Adaptive Biometrics; Biometrics; Continuous Authentication; ECG; Re-enrollment; Biotechnology; Artificial Intelligence; Computational Theory and Mathematics
Elenco autori:
R. Donida Labati, V. Piuri, R. Sassi, F. Scotti, G. Sforza
Link alla scheda completa:
Link al Full Text:
Titolo del libro:
Computational Intelligence in Biometrics and Identity Management (CIBIM), 2014 IEEE Symposium on