Out-of-Distribution Representation and Graph Neural Network Fusion Learning for ECG Biometrics

The electrocardiogram (ECG) signal, a promising trait in biometrics, has been extensively studied. While the deep learning-based model has demonstrated strong performance for ECG biometrics, several challenges remain, including efficient extraction of 1D signals’ topological properties and efficient use of signal distribution information at different times. Another challenge is adapting to the critical role of specific deep neural networks. To address these issues, this study proposes an out-of-distribution representation and graph neural network fusion learning (ORGNNFL) method for ECG biometrics. The ORGNNFL is mainly composed of a two-branch deep learning model for ECG biometrics, capable of learning to discriminate features from latent distributions and extracting topological features of 1D ECG signals. The multi-feature attention module is also proposed to adaptively capture valuable information from two-branch deep learning data for ECG biometrics. Experiments conducted on the four databases demonstrate that our method outperforms the state-of-the-art techniques, paving the way for a new era in ECG biometrics. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/matianbang/ORGNNFL</uri>.