Robust multi-feature collective non-negative matrix factorization for ECG biometrics

Abstract The field of electrocardiogram (ECG) biometrics has received considerable attention in recent years. Although some promising methods have been proposed, it is challenging to design a robust and precise method to improve the recognition performance of ECG signals with noise and sample variation. While the advantage of improved local binary pattern (LBP) for establishing identities has been widely recognized, extracting the latent semantics from multiple LBP features has attracted little attention. We propose a robust multi-feature collective non-negative matrix factorization (RMCNMF) model to handle noise and sample variation in ECG Biometrics. We extract multiple LBP histograms as feature descriptors from segmented ECG signals, and propose a multi-feature learning framework that learns unified representations in the shared latent semantic space via collective non-negative matrix factorization. To further enhance the discrimination of learned representations, we integrate label information and multiple norms in the proposed model, which not only preserves intra- and inter-subject similarities but also mitigates the influence of noise and sample variation. RMCNMF can be solved by an efficient iteration method, for which we provide a convergence analysis in detail. Extensive experiments on four ECG databases show that it performs competitively with state-of-the-art methods.