ECG Biometrics via Dual-Level Features with Collaborative Embedding and Dimensional Attention Weight Learning.

In recent years, electrocardiogram (ECG) biometrics has received extensive attention and achieved a series of exciting results. In order to achieve optimal ECG biometric recognition, it is crucial to effectively process the original ECG signals. However, most existing methods only focus on extracting features from one-dimensional time series, limiting the discriminability of individual identification to some extent. To overcome this limitation, we propose a novel framework that integrates dual-level features, i.e., 1D (time series) and 2D (relative position matrix) representations, through collaborative embedding, dimensional attention weight learning, and projection matrix learning. Specifically, we leverage collective matrix factorization to learn the shared latent representations by embedding dual-level features to fully mine these two kinds of features and preserve as much information as possible. To further enhance the discrimination of learned representations, we preserve the diverse information for different dimensions of the latent representations by means of dimensional attention weight learning. In addition, the learned projection matrix simultaneously facilitates the integration of dual-level features and enables the transformation of out-of-sample queries into the discriminative latent representation space. Furthermore, we propose an effective and efficient optimization algorithm to minimize the overall objective loss. To evaluate the effectiveness of our learned latent representations, we conducted experiments on two benchmark datasets, and our experimental results show that our method can outperform state-of-the-art methods.