Multi-Feature Sparse Representations Learning via Collective Matrix Factorization for ECG Biometric Recognition

Electrocardiogram (ECG) signal is a promising biometric trait, and many methods have been proposed for ECG biometric recognition. However, it is challenging to design a robust and precise method to improve the recognition performance of ECG signals with noise and signal variation. We present a multi-feature sparse representations learning model via collective matrix factorization for ECG biometric recognition, MSRCMF for short. First, we extract one-dimensional local binary pattern (1D-LBP), shape and wavelet features of ECG signals and then obtain their sparse representations. Second, to extract discriminative information and preserve the intra- and inter-subject similarities, we leverage the collective matrix factorization on multiple sparse representations and the label information to obtain the latent semantic space. At last, we can recognize the ECG signals in the learned semantic space. Extensive experiments on four ECG databases show that MSRCMF can achieve competitive performance compared to state-of-the-art methods.