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.

翻译：本研究对大规模心电信号（ECG）生物特征识别进行系统探究，直接针对当前文献中的一个关键空白：缺乏采用可操作度量标准与协议的大规模评估体系，而这种体系对于实现跨研究的有意义标准化与比较至关重要。我们首先证明身份信息已存在于表格化表征（基准特征）中：即使采用简单的基于多层感知机的嵌入网络也能获得显著性能，这为后续波形建模建立了强有力的基线。随后我们采用基于嵌入的深度学习模型（ArcFace），先在特征数据上训练，再直接应用于ECG波形数据。实验表明：从表格输入转向波形数据时性能显著提升，而扩大训练集规模并保持训练/验证/测试集间标准化的一致性可带来进一步增益。在大规模测试集上，验证任务在严格错误接受率阈值下获得高真实接受率（FAR=1e-3时TAR=0.908；FAR=1e-4时TAR=0.820），等错误率EER=2.53%（全对全测试）；闭集识别任务获得Rank@1=0.812与Rank@10=0.910。在开集场景中，采用两阶段流程（基于嵌入的top-$K$候选筛选+重排序）在FAR=1e-3与1e-4阈值下DIR@FAR最高可达0.976。总体而言，研究结果证实ECG携带可测量的个体特征签名，且大规模测试对于获得真实可比度量指标至关重要。本研究提供了基于实际操作的基准框架，有助于推动不同评估协议的标准化进程。