Verification and Identification in ECG biometric on large-scale

Abstract

This work studies electrocardiogram (ECG) biometrics at large scale, directly addressing a critical gap in the literature: the scarcity of large-scale evaluations with operational metrics and protocols that enable meaningful standardization and comparison across studies. We show that identity information is already present in tabular representations (fiducial features): even a simple MLP-based embedding network yields non-trivial performance, establishing a strong baseline before waveform modeling. We then adopt embedding-based deep learning models (ArcFace), first on features and then on ECG waveforms, showing a clear performance jump when moving from tabular inputs to waveforms, and a further gain with larger training sets and consistent normalization across train/val/test. On a large-scale test set, verification achieves high TAR at strict FAR thresholds (TAR=0.908 @ FAR=1e-3; TAR=0.820 @ FAR=1e-4) with EER=2.53\% (all-vs-all); closed-set identification yields Rank@1=0.812 and Rank@10=0.910. In open-set, a two-stage pipeline (top-$K$ shortlist on embeddings + re-ranking) reaches DIR@FAR up to 0.976 at FAR=1e-3 and 1e-4. Overall, the results show that ECG carries a measurable individual signature and that large-scale testing is essential to obtain realistic, comparable metrics. The study provides an operationally grounded benchmark that helps standardize evaluation across protocols.