Electrocardiogram (ECG) biometric recognition has emerged as a hot research topic in the past decade. Although some promising results have been reported, especially using sparse representation learning (SRL) and deep neural network, robust identification for small-scale data is still a challenge. To address this issue, we integrate SRL into a deep cascade model, and propose a multi-scale deep cascade bi-forest (MDCBF) model for ECG biometric recognition. We design the bi-forest based feature generator by fusing L1-norm sparsity and L2-norm collaborative representation to efficiently deal with noise. Then we propose a deep cascade framework, which includes multi-scale signal coding and deep cascade coding. In the former, we design an adaptive weighted pooling operation, which can fully explore the discriminative information of segments with low noise. In deep cascade coding, we propose level-wise class coding without backpropagation to mine more discriminative features. Extensive experiments are conducted on four small-scale ECG databases, and the results demonstrate that the proposed method performs competitively with state-of-the-art methods.

心电图 (ECG) 生物特征识别已成为过去十年的热门研究课题。尽管已经报道了一些有希望的结果，特别是使用稀疏表示学习 (SRL) 和深度神经网络，但对小规模数据的鲁棒识别仍然是一个挑战。为了解决这个问题，我们将 SRL 集成到一个深度级联模型中，并提出了一种用于心电生物识别的多尺度深度级联双森林（MDCBF）模型。我们通过融合 L1 范数稀疏性和 L2 范数协同表示来有效地处理噪声，从而设计了基于双森林的特征生成器。然后我们提出了一个深度级联框架，包括多尺度信号编码和深度级联编码。在前者中，我们设计了一个自适应加权池化操作，可以充分挖掘低噪声段的判别信息。在深度级联编码中，我们提出了没有反向传播的逐级类编码，以挖掘更多判别特征。在四个小规模 ECG 数据库上进行了大量实验，结果表明所提出的方法与最先进的方法相比具有竞争力。