Kui-Kui Wang, Gong-Ping Yang, Lu Yang, Yu-Wen Huang, Yi-Long Yin. ECG Biometrics via Enhanced Correlation and Semantic-rich Embedding. Machine Intelligence Research. https://doi.org/10.1007/s11633-022-1345-0
Citation: Kui-Kui Wang, Gong-Ping Yang, Lu Yang, Yu-Wen Huang, Yi-Long Yin. ECG Biometrics via Enhanced Correlation and Semantic-rich Embedding. Machine Intelligence Research. https://doi.org/10.1007/s11633-022-1345-0

ECG Biometrics via Enhanced Correlation and Semantic-rich Embedding

doi: 10.1007/s11633-022-1345-0
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  • Author Bio:

    Kui-Kui Wang received the M. Sc. degree in computer science and technology from Shandong University, China in 2017. Currently, she is a Ph. D. degree candidate in software engineering at School of Software Engineering, Shandong University, China. Her research interests include pattern recognition, biometrics, and machine learning. E-mail: sarahkuikui@163.com ORCID iD: 0000-0002-0790-0736

    Gong-Ping Yang received the Ph. D. degree in computer software and theory from Shandong University, China in 2007. He is currently a professor at School of Software Engineering, Shandong University, China and an adjunct professor at School of Computer, Heze University, China. His research interests include pattern recognition, image processing, and biometrics. E-mail: gpyang@sdu.edu.cn (Corresponding author) ORCID iD: 0000-0001-7637-2749

    Lu Yang received the Ph. D. degree in computer science and technology from Shandong University, China in 2016. Now she is a professor with School of Computer Science and Technology, Shandong Jianzhu University, China. Her research interests include biometrics and machine learning. E-mail: yangluhi@163.com

    Yu-Wen Huang received the Ph. D. degree in computer science and technology from Shandong University, China in 2021. Now he is an associate professor with School of Computer, Heze University, China. His research interests include ECG recognition, biometrics and machine learning. E-mail: hzxy_hyw@163.com

    Yi-Long Yin received the Ph. D. degree in agricultural mechanization engineering from Jilin University, China in 2000. He is the director of the Machine Learning and Data Mining Group and a professor with Shandong University, China. From 2000 to 2002, he was a post-doctoral fellow with Department of Electronic Science and Engineering, Nanjing University, China. His research interests include machine learning, data mining and computer vision. E-mail: ylyin@sdu.edu.cn

  • Received Date: 2022-04-06
  • Accepted Date: 2022-06-06
  • Publish Online: 2023-01-11
  • Electrocardiogram (ECG) biometric recognition has gained considerable attention, and various methods have been proposed to facilitate its development. However, one limitation is that the diversity of ECG signals affects the recognition performance. To address this issue, in this paper, we propose a novel ECG biometrics framework based on enhanced correlation and semantic-rich embedding. Firstly, we construct an enhanced correlation between the base feature and latent representation by using only one projection. Secondly, to fully exploit the semantic information, we take both the label and pairwise similarity into consideration to reduce the influence of ECG sample diversity. Furthermore, to solve the objective function, we propose an effective and efficient algorithm for optimization. Finally, extensive experiments are conducted on two benchmark datasets, and the experimental results show the effectiveness of our framework.

     

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