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Gram Matrix-Based Convolutional Neural Network for Biometric Identification Using Photoplethysmography Signal

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Abstract

As a kind of physical signals that could be easily acquired in daily life, photoplethysmography (PPG) signal becomes a promising solution to biometric identification for daily access management system (AMS). State-of-the-art PPG-based identification systems are susceptible to the form of motions and physical conditions of the subjects. In this work, to exploit the advantage of deep learning, we developed an improved deep convolutional neural network (CNN) architecture by using the Gram matrix (GM) technique to convert time-serial PPG signals to two-dimensional images with a temporal dependency to improve accuracy under different forms of motions. To ensure a fair evaluation, we have adopted cross-validation method and “training and testing” dataset splitting method on the TROIKA dataset collected in ambulatory conditions. As a result, the proposed GM-CNN method achieved accuracy improvement from 69.5% to 92.4%, which is the best result in terms of multi-class classification compared with state-of-the-art models. Based on average five-fold cross-validation, we achieved an accuracy of 99.2%, improved the accuracy by 3.3% compared with the best existing method for the binary-class.

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Authors and Affiliations

  1. Department of Micro-Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China

    Caiyu Wu  (吴彩钰), Sabor Nabil, Shihong Zhou  (周世鸿), Min Wang  (王 敏) & Guoxing Wang  (王国兴)

  2. MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, 200240, China

    Caiyu Wu  (吴彩钰), Sabor Nabil, Shihong Zhou  (周世鸿), Min Wang  (王 敏) & Guoxing Wang  (王国兴)

  3. Electrical Engineering Department, Assiut University, Assiut, 71516, Egypt

    Sabor Nabil

  4. Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China

    Liang Ying  (应 亮)

Authors
  1. Caiyu Wu  (吴彩钰)
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  2. Sabor Nabil
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  3. Shihong Zhou  (周世鸿)
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  4. Min Wang  (王 敏)
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  5. Liang Ying  (应 亮)
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  6. Guoxing Wang  (王国兴)
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Corresponding author

Correspondence to Guoxing Wang  (王国兴).

Additional information

Foundation item: the National Key R&D Program of China (No. 2019YFB2204500), and the Translational Medicine Cross Research Fund of Shanghai Jiao Tong University (No. ZH2018QNB22)

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Wu, C., Nabil, S., Zhou, S. et al. Gram Matrix-Based Convolutional Neural Network for Biometric Identification Using Photoplethysmography Signal. J. Shanghai Jiaotong Univ. (Sci.) 27, 463–472 (2022). https://doi.org/10.1007/s12204-022-2426-5

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  • DOI: https://doi.org/10.1007/s12204-022-2426-5

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