1DIEN: Cross-session Electrocardiogram Authentication Using 1D Integrated EfficientNet

Abstract

The potential of using electrocardiogram (ECG), an important physiological signal for humans, as a new biometric trait has been demonstrated, and ongoing efforts have focused on utilizing deep learning (e.g., 2D neural networks) to improve authentication accuracy (with some efficiency tradeoffs). In most of the existing ECG-based authentication approaches, the ECG recordings for enrollment and testing are collected within short intervals (e.g., within an hour). However, since ECG biometrics change over time, this design may decrease authentication accuracy when ECG recordings are collected weeks or even months prior. In this article, we propose 1D Integrated EfficientNet (1DIEN) to achieve cross-session ECG authentication. We adopt 1D neural networks as a lightweight alternative to 2D neural networks, and a voting scheme is designed to reduce variance and improve general authentication performance. We use three public ECG databases (i.e., an inter-session database, a mixed-session database, and an intra-session database) to evaluate our proposed 1DIEN under different authentication scenarios. The experimental results show that our approach achieves satisfactory performance for ECG authentication at a 3-month interval and is suitable for practical applications.