Photoplethysmography Biometric Recognition Using Deep Learning

Cherry, Ali; Abbani, Mohammad; Sleiman, Ali; Hage-Diab, Ali; Hajj-Hassan, Mohamad

doi:10.1109/icabme59496.2023.10293049

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Seok et al [13] proposed a one-dimensional twin neural network biometric model based on PPG, which reduced noise and retained individual unique characteristics through the multi-period averaging method, achieving efficient and safe identification and authenti-cation. Abbani et al [14] used a two-way long short-term memory deep learning algorithm in their research and successfully designed an identity authentication model based on PPG signals. Dwaipayan et al [15] designed a novel deep learning model, CorNET, which combines two convolutional neural network layers and two long short-term memory layers for identity authentication tasks.…”

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%