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.

中文翻译：

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学习联合模式和特定模式：非常规心电图生物特征识别的统一稀疏表示

智能手机和平板电脑等设备引起了人们对非常规心电图（ECG）生物特征识别的兴趣。尽管使用多特征信息建立身份的优势已得到广泛认可，但用于多特征生物特征识别的计算稀疏表示模型直到最近才受到更多关注。我们提出了一个统一的稀疏表示框架，该框架可共同利用联合的和特定的模式进行心电图生物识别。特别是，与仅考虑多个特征的稀疏模式之间的一致性的联合稀疏表示不同，我们将一致约束和成对约束结合在一起，不仅可以学习所有特征的潜在判别表示，而且可以捕获它们之间的交互。此外，我们的框架具有通用性，只需调整正则化参数即可轻松适应其他多功能稀疏表示模型。通过有效的乘数交替方向方法（ADMM）解决了优化问题。在两个公开可用的个人数据集上进行的大量实验表明，与最新的ECG生物特征识别方法相比，我们的方法可以实现竞争甚至更高的性能。