Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition

Chun-Ying Liu; Gong-Ping Yang; Yu-Wen Huang; Fu-Xian Huang

doi:10.1007/s11633-022-1366-8

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Chun-Ying Liu, Gong-Ping Yang, Yu-Wen Huang, Fu-Xian Huang. Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition. Machine Intelligence Research. https://doi.org/10.1007/s11633-022-1366-8

Citation:

Chun-Ying Liu, Gong-Ping Yang, Yu-Wen Huang, Fu-Xian Huang. Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition. Machine Intelligence Research. https://doi.org/10.1007/s11633-022-1366-8

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Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition

doi: 10.1007/s11633-022-1366-8

Chun-Ying Liu^{1

,},
Gong-Ping Yang^{2

,},
Yu-Wen Huang^{1

,},
Fu-Xian Huang^{1

,}

1.
School of computer, Heze University, Heze 274015, China
2.
School of Software, Shandong University, Jinan 250101, China

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Author Bio:
Chun-Ying Liu received the M. Eng. degree in computer science from Shandong University of Science and Technology, China in 2008. She is now an associate professor at School of Computer, Heze University, China.Her research interests include biometrics, data-mining and pattern recognition.E-mail: lcy810204@163.comORCID iD: 0000-0002-6062-4224

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, 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.cnORCID iD: 0000-0001-7637-2749

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 at School of Computer, Heze University, China.His research interests include ECG recognition, biometrics and machine learning.E-mail: hzxy_hyw@163.com

Fu-Xian Huang received the M. Eng. degree in computer science from Shandong University of Science and Technology, China in 2005. He is now a professor at School of Computer, Heze University, China.His research interests include biometrics and machine learning.E-mail: huangfuxian@126.com (Corresponding author)ORCID iD: 0000-0002-2838-9973
Received Date: 2022-04-26
Accepted Date: 2022-08-08
Publish Online: 2023-01-11

Abstract

Abstract

Photoplethysmography (PPG) biometrics have received considerable attention. Although deep learning has achieved good performance for PPG biometrics, several challenges remain open: 1) How to effectively extract the feature fusion representation from time and frequency PPG signals. 2) How to effectively capture a series of PPG signal transition information. 3) How to extract time-varying information from one-dimensional time-frequency sequential data. To address these challenges, we propose a dual-domain and multiscale fusion deep neural network (DMFDNN) for PPG biometric recognition. The DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics, which can learn the time-varying and multiscale discriminative features from the time and frequency domains. Meanwhile, we design a multiscale extraction module to capture transition information, which consists of multiple convolution layers with different receptive fields for capturing multiscale transition information. In addition, the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG biometrics. Experiments on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.
- Photoplethysmography (PPG) signal,
- biometric recognition,
- multiple scale,
- deep neural network,
- dual-domain attention

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References(28)

References

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Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition

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