Z. Mizgalewicz, C. R. Cuenca, M. R. Rivolta, R. Donida Labati, F. Scotti, V. Piuri, R. Sassi, Minimal Preprocessing of ECG Signals for Deep Learning-Based Biometric Systems, IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2024), June 2024, Accepted.
Biometric systems
1D Physiological Signals: ECG and PPG
Recent studies in biometrics focus on one dimensional physiological signals commonly acquired in medical applications, like electrocardiogram (ECG), electroencephalograms (EEG), phonocardiogram (PCG), and photoplethysmogram (PPG). In this context, an important application is in continuous authentication scenarios since physiological signals are frequently captured for long time periods in order to monitor the health status of the patients.
With respect to other physiological signals, PPG data present some advantages since they can be captured using widely diffused, comfortable, and low-cost sensors.
We have performed a feasibility study on continuous authentication techniques based on PPG signals. We have realized a biometric recognition method based on a correlation approach. The accuracy of this method have been evaluated on different datasets describing signals of variable time duration. Finally, the performance of continuous enrollment strategies have been investigated.
The obtained results suggest that PPG signals present sufficient discriminability to be used in biometric applications that do not require very high accuracy. Moreover, the use of continuous enrollment strategies can improve the performance of continuous authentication systems.
With respect to other physiological signals, PPG data present some advantages since they can be captured using widely diffused, comfortable, and low-cost sensors.
We have performed a feasibility study on continuous authentication techniques based on PPG signals. We have realized a biometric recognition method based on a correlation approach. The accuracy of this method have been evaluated on different datasets describing signals of variable time duration. Finally, the performance of continuous enrollment strategies have been investigated.
The obtained results suggest that PPG signals present sufficient discriminability to be used in biometric applications that do not require very high accuracy. Moreover, the use of continuous enrollment strategies can improve the performance of continuous authentication systems.
Relevant publications
R. Donida Labati, V. Piuri, F. Rundo, F. Scotti, MultiCardioNet: Interoperability between ECG and PPG biometrics, Pattern Recognition Letters, vol. 175, pp. 1-7, 2023, ISSN 0167-8655.
R. Donida Labati, E. Muñoz, V. Piuri, R. Sassi, F. Scotti, Deep-ECG: Convolutional Neural Networks for ECG biometric recognition, Pattern Recognition Letters, vol. 126, pp. 78-85, Elsevier, September 2019, ISSN 0167-8655.
R. Donida Labati, V. Piuri, R. Sassi, G. Sforza, F. Scotti, Adaptive ECG biometric recognition: a study on re-enrollment methods for QRS signals, Proc. of the IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM 2014), pp. 30-37, Orlando, FL, USA, December 2014, ISSN 978-1-4799-4533-7/14.
R. Donida Labati, V. Piuri, R. Sassi, F. Scotti, HeartCode: a novel binary ECG-based template, Proc. of the IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BioMS 2014), pp. 86-91, Rome, Italy, October 2014, ISSN 978-1-4799-5175-8.
R. Donida Labati, R. Sassi, F. Scotti, ECG biometric recognition: permanence analysis of QRS signals for 24 hours continuous authentication, Proc. of the IEEE Int. Workshop on Information Forensics and Security (WIFS 2013), Guangzhou, China, November 2013, ISSN 978-1-4673-5593-3.
R. Donida Labati, V. Piuri, F. Rundo, F. Scotti, Photoplethysmographic biometrics: A comprehensive survey, Pattern Recognition Letters, vol. 156, pp. 119-125, April 2022, ISSN 0167-8655.
A. Bonissi, R. Donida Labati, L. Perico, R. Sassi, F. Scotti, L. Sparagino, A preliminary study on continuous authentication methods for photoplethysmographic biometrics, Proc. of the 2013 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BioMS 2013), pp. 28-33, Napoli, Italy, September 2013, ISSN 978-1-4799-0625-3.