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

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

doi: 10.1007/s11633-022-1366-8
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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
  • 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.

     

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  • [1]
    D. Y. Hwang, B. Taha, D. S. Lee, D. Hatzinakos. Evaluation of the time stability and uniqueness in PPG-based biometric system. IEEE Transactions on Information Forensics and Security, vol. 16, pp. 116–130, 2021. DOI: 10.1109/TIFS.2020.3006313.
    [2]
    D. E. Mancilla-Palestina, J. A. Jimenez-Duarte, J. M. Ramirez-Cortes, A. Hernandez, P. Gomez-Gil, J. Rangel-Magdaleno. Embedded system for bimodal biometrics with fiducial feature extraction on ECG and PPG signals. In Proceedings of IEEE International Instrumentation and Measurement Technology Conference, Dubrovnik, Croatia, 2020. DOI: 10.1109/I2MTC43012.2020.9128394.
    [3]
    C. Y. Liu, J. J. Yu, Y. W. Huang, F. X. Huang. Time–frequency fusion learning for photoplethysmography biometric recognition. IET Biometrics, vol. 11, no. 3, pp. 187–198, 2022. DOI: 10.1049/bme2.12070.
    [4]
    J. Luque, G. Cortès, C. Segura, A. Maravilla, J. Esteban, J. Fabregat. End-to-end Photoplethysmography (PPG) based biometric authentication by using convolutional neural networks. In Proceedings of the 26th European Signal Processing Conference, IEEE, Rome, Italy, pp. 538–542, 2018. DOI: 10.23919/EUSIPCO.2018.8553585.
    [5]
    L. Everson, D. Biswas, M. Panwar, D. Rodopoulos, A. Acharyya, C. H. Kim, C. Van Hoof, M. Konijnenburg, N. Van Helleputte. BiometricNet: Deep learning based biometric identification using wrist-worn PPG. In Proceedings of International Symposium on Circuits and Systems, IEEE, Florence, Italy, 2018. DOI: 10.1109/ISCAS.2018.8350983.
    [6]
    D. Biswas, L. Everson, M. Q. Liu, M. Panwar, B. E. Verhoef, S. Patki, C. H. Kim, A. Acharyya, C. Van Hoof, M. Konijnenburg, N. Van Helleputte. CorNET: Deep learning framework for PPG-based heart rate estimation and biometric identification in ambulant environment. IEEE Transactions on Biomedical Circuits and Systems, vol. 13, no. 2, pp. 282–291, 2019. DOI: 10.1109/TBCAS.2019.2892297.
    [7]
    D. Y. Hwang, B. Taha, D. Hatzinakos. PBGAN: Learning PPG representations from GAN for time-stable and unique verification system. IEEE Transactions on Information Forensics and Security, vol. 16, pp. 5124–5137, 2021. DOI: 10.1109/TIFS.2021.3122817.
    [8]
    Y. L. Ye, G. C. Xiong, Z. Y. Wan, T. J. Pan, Z. W. Huang. PPG-based biometric identification: Discovering and identifying a new user. In Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Mexico, pp. 1145–1148, 2021. DOI: 10.1109/EMBC46164.2021.9630883.
    [9]
    X. C. Liu, Z. H. Yuan, D. L. Ma. Deep learning framework for biometric identification from wrist-worn PPG with acceleration signals. In Proceedings of the 6th International Conference on Signal and Image Processing, IEEE, Nanjing, China, pp. 1–5, 2021. DOI: 10.1109/ICSIP52628.2021.9688605.
    [10]
    J. C. Yao, X. D. Sun, Y. B. Wan. A pilot study on using derivatives of photoplethysmographic signals as a biometric identifier. In Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, pp. 4576–4579, 2007. DOI: 10.1109/IEMBS.2007.4353358.
    [11]
    S. Chakraborty, S. Pal. Photoplethysmogram signal based biometric recognition using linear discriminant classifier. In Proceedings of the 2nd International Conference on Control, Instrumentation, Energy & Communication, IEEE, Kolkata, India, pp. 183–187, 2016. DOI: 10.1109/CIEC.2016.7513792.
    [12]
    N. I. M. Nadzri, K. A. Sidek, A. F. Ismail. Biometric recognition for twins inconsideration of age variability using PPG signals. Journal of Telecommunication,Electronic and Computer Engineering, vol. 10, no. 1–5, pp. 97–100, 2018.
    [13]
    J. Sancho, Á. Alesanco, J. García. Biometric authentication using the PPG: A long-term feasibility study. Sensors, vol. 18, no. 5, Article number 1525, 2018. DOI: 10.3390/s18051525.
    [14]
    P. Spachos, J. X. Gao, D. Hatzinakos. Feasibility study of photoplethysmographic signals for biometric identification. In Proceedings of the 17th International Conference on Digital Signal Processing, IEEE, Corfu, Greece, 2011. DOI: 10.1109/ICDSP.2011.6004938.
    [15]
    N. Karimian, M. Tehranipoor, D. Forte. Non-fiducial PPG-based authentication for healthcare application. In Proceedings of the IEEE/EMBS International Conference on Biomedical & Health Informatics, IEEE, Orlando, USA, pp. 429–432, 2017. DOI: 10.1109/BHI.2017.7897297.
    [16]
    U. Yadav, S. N. Abbas, D. Hatzinakos. Evaluation of PPG biometrics for authentication in different states. In Proceedings of International Conference on Biometrics, IEEE, Gold Coast, Australia, pp. 277–282, 2018. DOI: 10.1109/ICB2018.2018.00049.
    [17]
    P. Farago, R. Groza, L. Ivanciu, S. Hintea. A correlation-based biometric identification technique for ECG, PPG and EMG. In Proceedings of the 42nd International Conference on Telecommunications and Signal Processing, IEEE, Budapest, Hungary, pp. 716–719, 2019. DOI: 10.1109/TSP.2019.8768810.
    [18]
    S. W. Lee, D. K. Woo, Y. K. Son, P. S. Mah. Wearable bio-signal (PPG)-based personal authentication method using random forest and period setting considering the feature of PPG signals. Journal of Computers, vol. 14, no. 4, pp. 283–294, 2019. DOI: 10.17706/jcp.14.4.283-294.
    [19]
    K. Dragomiretskiy, D. Zosso. Variational mode decomposition. IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531–544, 2014. DOI: 10.1109/TSP.2013.2288675.
    [20]
    J. F. Yang, Y. W. Huang, R. L. Zhang, F. X. Huang, Q. G. Meng, S. X. Feng. Study on PPG biometric recognition based on multifeature extraction and naive Bayes classifier. Scientific Programming, vol. 2021, Article number 5597624, 2021. DOI: 10.1155/2021/5597624.
    [21]
    S. Nikan, F. Gwadry-Sridhar, M. Bauer. Pattern recognition application in ECG arrhythmia classification. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies, Porto, Portugal, pp. 48–56, 2017. DOI: 10.5220/0006116300480056.
    [22]
    R. Raj, J. Selvakumar, M. Anburajan. Evaluation of hypotension using wavelet and time frequency analysis of photoplethysmography (PPG) signal. In Proceedings of International Conference on Advances in Computational Intelligence in Communication, Puducherry, India, vol. 14, pp. 57–61, 2016.
    [23]
    M. Y. Bian, B. Peng, W. Wang, J. Dong. An accurate LSTM based video heart rate estimation method. In Proceedings of the 2nd Pattern Recognition and Computer Vision. Springer, Xi′an, China, pp. 409–417, 2019. DOI: 10.1007/978-3-030-31726-3_35.
    [24]
    Z. Y. Jia, Y. F. Lin, J. Wang, X. H. Wang, P. Y. Xie, Y. B. Zhang. SalientSleepNet: Multimodal salient wave detection network for sleep staging. In Proceedings of the 30th International Joint Conference on Artificial Intelligence, Montreal, Canada, pp. 2614–2620, 2021. DOI: 10.24963/ijcai.2021/360.
    [25]
    M. A. F. Pimentel, A. E. W. Johnson, P. H. Charlton, D. Birrenkott, P. J. Watkinson, L. Tarassenko, D. A. Clifton. Toward a robust estimation of respiratory rate from pulse oximeters. IEEE Transactions on Biomedical Engineering, vol. 64, no. 8, pp. 1914–1923, 2017. DOI: 10.1109/TBME.2016.2613124.
    [26]
    A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, H. E. Stanley. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, vol. 101, no. 23, pp. e215–e220, 2000. DOI: 10.1161/01.CIR.101.23.e215.
    [27]
    J. F. Yang, Y. W. Huang, F. X. Huang, G. P. Yang. Photoplethysmography biometric recognition model based on sparse softmax vector and k-nearest neighbor. Journal of Electrical and Computer Engineering, vol. 2020, Article number 9653470, 2020. DOI: 10.1155/2020/9653470.
    [28]
    W. Karlen, S. Raman, J. M. Ansermino, G. A. Dumont. Multiparameter respiratory rate estimation from the photoplethysmogram. IEEE Transactions on Biomedical Engineering, vol. 60, no. 7, pp. 1946–1953, 2013. DOI: 10.1109/TBME.2013.2246160.
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