Deep learning-based photoplethysmography biometric authentication for continuous user verification

Wan, Li; Liu, Kechen; Mengash, Hanan Abdullah; Alruwais, Nuha; Duhayyim, Mesfer Al; Venkatachalam, K.

doi:10.1016/j.asoc.2024.111461

Cited by 3 publications

(1 citation statement)

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“…Deep learning methods can automatically learn useful features directly from raw data without requiring manual feature design and selection. Li et al [11] designed and trained a multi-scale feature fusion deep learning (MFFD) model. This model is mainly based on convolutional neural network architecture and is used to effectively extract the features of PPG signals and to learn how to accurately distinguish different individuals based on each person's unique PPG pattern.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Convolutional Neural Network Method for Two-Dimensional PhotoPlethysmoGraphy Signals

Zhao,

Zhang,

2024

Applied Sciences

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Data information security on wearable devices has emerged as a significant concern among users, so it becomes urgent to explore authentication methods based on wearable devices. Using PhotoPlethysmoGraphy (PPG) signals for identity authentication has been proven effective in biometric authentication. This paper proposes a convolutional neural network authentication method based on 2D PPG signals applied to wearable devices. This method uses Markov Transition Field technology to convert one-dimensional PPG signal data into two-dimensional image data, which not only retains the characteristics of the signal but also enriches the spatial information. Afterward, considering that wearable devices usually have limited resources, a lightweight convolutional neural network model is also designed in this method, which reduces resource consumption and computational complexity while ensuring high performance. It is proved experimentally that this method achieves 98.62% and 96.17% accuracy on the training set and test set, respectively, an undeniable advantage compared to the traditional one-dimensional deep learning method and the classical two-dimensional deep learning method.

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Section: Introductionmentioning

confidence: 99%