STERLING: Towards Effective ECG Biometric Recognition

Electrocardiogram (ECG) biometric recognition has recently attracted considerable attention and various promising approaches have been proposed. However, due to the real nonstationary ECG noise environment, it is still challenging to perform this technique robustly and precisely. In this paper, we propose a novel ECG biometrics framework named robuSt semanTic spacE leaRning with Local similarity preserviNG (STERLING) to learn a latent space where ECG signals can be robustly and discriminatively represented with semantic information and local structure being preserved. Specifically, in the proposed framework, a novel loss function is proposed to learn robust semantic representation by introducing l(2,1)-norm loss and making full use of the supervised information. In addition, a graph regularization is imposed to preserve the local structure information in each subject. Finally, in the learnt latent space, matching can be effectively done. The experimental results on three widely-used datasets indicate that the proposed framework can outperform the state-of-the-arts.