Learning Joint and Specific Patterns: A Unified Sparse Representation for Off-the-Person ECG Biometric Recognition

Devices such as smartphones and tablets have spurred interest in off-the-person electrocardiogram (ECG) biometric recognition. While the advantage of using multi-feature information for establishing identities has been widely recognized, computational sparse representation models for multi-feature biometric recognition have only recently received more attention. We propose a unified sparse representation framework which collaboratively exploits joint and specific patterns for ECG biometric recognition. In particular, unlike joint sparse representation, which only considers the consistency among sparsity patterns of multiple features, we combine the consistent and pairwise constraints, which not only learn latent discriminant representations for all features but capture the interactions between them. In addition, our framework is universal and easily adapts to other multi-feature sparse representation models by just tuning the regularization parameters. The optimization problem is solved by an efficient alternating direction method of multipliers (ADMM). Extensive experiments on two publicly available off-the-person datasets demonstrate that our method can achieve competitive or even superior performance compared to state-of-the-art ECG biometric recognition methods.