Abstract Electrocardiogram (ECG) biometrics has recently received considerable attention and is considered to be a promising biometric trait. Although some promising results on ECG biometrics have been reported, it is still challenging to perform this technique robustly and precisely. To address these issues, this paper presents a novel ECG biometrics framework: Multi-Scale Differential Feature for ECG biometrics with Collective Matrix Factorization (CMF). First, we extract the Multi-Scale Differential Feature (MSDF) from the one-dimensional ECG signal and then fuse MSDF with 1DMRLBP to generate the MSDF-1DMRLBP, which acts as the base feature of the ECG signal. Second, to extract discriminative information from the intermediate base features, we leverage the CMF technique to generate the final robust ECG representations by simultaneously embedding MSDF-1DMRLBP and label information. Consequently, the final robust features could preserve the intra-subject and inter-subject similarities. Extensive experiments are conducted on four ECG databases, and the results demonstrate that the proposed method can outperform the state-of-the-art in terms of both accuracy and efficiency.

中文翻译：

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具有集体矩阵分解的 ECG 生物特征的多尺度微分特征

摘要 心电图 (ECG) 生物识别最近受到了相当多的关注，被认为是一种很有前途的生物识别特征。尽管已经报告了心电图生物识别技术的一些有希望的结果，但稳健而精确地执行这项技术仍然具有挑战性。为了解决这些问题，本文提出了一种新的 ECG 生物识别框架：具有集体矩阵分解 (CMF) 的 ECG 生物识别的多尺度差分特征。首先，我们从一维 ECG 信号中提取多尺度差分特征 (MSDF)，然后将 MSDF 与 1DMRLBP 融合以生成 MSDF-1DMRLBP，作为 ECG 信号的基本特征。其次，从中间基础特征中提取判别信息，我们利用 CMF 技术通过同时嵌入 MSDF-1DMRLBP 和标签信息来生成最终稳健的 ECG 表示。因此，最终的稳健特征可以保留主体内和主体间的相似性。在四个 ECG 数据库上进行了广泛的实验，结果表明所提出的方法在准确性和效率方面都可以优于最先进的方法。