Joint Dual-Domain Matrix Factorization for ECG Biometric Recognition

Abstract

Electrocardiogram (ECG) biometrics has aroused extensive attention in the research field of biometric recognition. How-ever, most existing methods either only consider a single do-main (time domain or frequency domain) to extract features or extract multi-features while ignoring the specific proper-ties of each domain. In this paper, we propose a novel ECG biometrics framework termed Joint Dual-domain Matrix Factorization (JDMF). JDMF learns latent spaces for each do-main by exploring the cross-correlations between them and preserving domain-specific properties. To endow the latent spaces with more powerful representation capabilities, JDMF further makes full use of the supervised information and could automatically learn the weights of domains. The experimental results on two widely-used datasets indicate that the proposed framework can outperform state-of-the-arts.