  In recent years, physiological signal based authentication has shown great
promises,for its inherent robustness against forgery. Electrocardiogram (ECG)
signal, being the most widely studied biosignal, has also received the highest
level of attention in this regard. It has been proven with numerous studies
that by analyzing ECG signals from different persons, it is possible to
identify them, with acceptable accuracy. In this work, we present, EDITH, a
deep learning-based framework for ECG biometrics authentication system.
Moreover, we hypothesize and demonstrate that Siamese architectures can be used
over typical distance metrics for improved performance. We have evaluated EDITH
using 4 commonly used datasets and outperformed the prior works using less
number of beats. EDITH performs competitively using just a single heartbeat
(96-99.75% accuracy) and can be further enhanced by fusing multiple beats (100%
accuracy from 3 to 6 beats). Furthermore, the proposed Siamese architecture
manages to reduce the identity verification Equal Error Rate (EER) to 1.29%. A
limited case study of EDITH with real-world experimental data also suggests its
potential as a practical authentication system.
