Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (3): 617-632.doi: 10.1007/s11390-021-1033-5

Special Issue: Artificial Intelligence and Pattern Recognition

• Special Section on Learning from Small Samples • Previous Articles     Next Articles

Multi-Scale Deep Cascade Bi-Forest for Electrocardiogram Biometric Recognition

Yu-Wen Huang1,2, Member, CCF, Gong-Ping Yang1,2,*, Senior Member, CCF, Kui-Kui Wang1, Hai-Ying Liu3, and Yi-Long Yin1, Senior Member, CCF        

  1. 1 School of Software, Shandong University, Jinan 250101, China;
    2 School of Computer, Heze University, Heze 274015, China;
    3 Department of Computer Engineering, Changji University, Changji 831100, China
  • Received:2020-09-29 Revised:2021-03-02 Online:2021-05-05 Published:2021-05-31
  • Contact: Gong-Ping Yang
  • About author:Yu-Wen Huang received his Master's degree in computer science from Guangxi Normal University, Guilin, in 2009. He is an associate professor in the School of Computer, Heze University, Heze, and pursuing his Ph.D. degree at Shandong University, Jinan. His research interests include ECG recognition, biometrics and machine learning.
  • Supported by:
    This work was supported in part by the NSFC-Xinjiang Joint Fund under Grant No. U1903127 and in part by the Natural Science Foundation of Shandong Province under Grant No. ZR2020MF052.

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.

Key words: electrocardiogram (ECG) biometric recognition; small-scale data; deep cascade bi-forest; multi-scale division; sparse representation learning;

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