ONLINE ECG BIOMETRICS VIA HADAMARD CODE

In recent years, Electrocardiogram (ECG) biometrics has gained extensive attention. However, most existing methods adopted offline batch learning, which means that they need to accumulate all data and retrain the model when new data comes. Therefore, it is inefficient and unpractical for them to handle the online scenario where new data may continually come. To overcome the above limitation, we propose a novel ECG biometrics framework, termed Online ECG Biometrics based on Hadamard Codes. Firstly, we leverage matrix factorization to learn discriminative representations for ECG signals from their base feature space. Considering to leverage the orthogonal property of the Hadamard matrix, we use it to construct Hadamard codes to represent individuals and further guide the learning of representations. Furthermore, we develop an online optimization algorithm, which is efficient and effective to investigate the incremental problem in the context of ECG biometrics. The experimental results on two benchmark datasets indicate the merits of the proposed framework over the state-of-the-art.