Permanence of ECG Biometric: Experiments Using Convolutional Neural Networks

Abstract

ECG biometric has emerged as an appealing biometric primarily because it is difficult to spoof. Because ECG is a continuous measure of an electrophysiological signal, it is difficult to mimic, but at the same time, its day-to-day variations impact its permanence. In this paper, we present a study of the permanence of ECG biometric using a Convolutional Neural Network based authentication system and a multi-session ECG dataset collected from 800 users. The authentication system achieved an equal error rate of 2% on ECG-ID database, improving the state-of-the-art. Using this system, we designed a series of rigorous experiments by varying the days elapsed between when enrollment and authentication are performed. The results show that, despite controlling for posture, equal error rate increases as days pass. Simply including more data to enrollment does improve the accuracy, but more recent data are significantly more advantageous.