Multi-Scale Differential Feature for ECG Biometrics with Collective Matrix Factorization

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.