BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets

Applied Sciences

Raul Sanchez-Reillo¹  Paloma Tirado-Martin¹ 

[1] University Group for Identification Technologies (GUTI), Carlos III University of Madrid, 28912 Leganés, Spain;

关键词: ECG biometrics;    Deep Learning;    pattern recognition;    human verification;    Convolutional Neural Networks;    Long-Short Term Memory;   

DOI  :  10.3390/app11135880

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown