RT Journal Article
T1 BioECG: Improving ECG biometrics with deep learning and enhanced datasets
A1 Tirado Martín, Paloma
A1 Sanchez-Reillo, Raul
AB Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise.
PB MDPI
SN 2076-3417
YR 2021
FD 2021-07-01
LK https://hdl.handle.net/10016/33800
UL https://hdl.handle.net/10016/33800
LA eng
DS e-Archivo
RD 18 jul. 2024