research-article

PPG-based human identification using Mel-frequency cepstral coefficients and neural networks

Authors:

Atef Abou Elazm,

Nirmeen A. El-Bahnasawy,

Ghada M. El Banby,

Fathi E. Abd El-SamieAuthors Info & Claims

Multimedia Tools and Applications, Volume 80, Issue 17

Pages 26001 - 26019

https://doi.org/10.1007/s11042-021-10781-8

Published: 01 July 2021 Publication History

Abstract

One of the known problems in security systems is to identify persons based on certain signatures. Biometrics have been adopted in security systems to identify persons based on some physiological or behavioral characteristics that they own. Photoplethysmography (PPG) is a physiological signal that is used to describe the volumetric change of blood flow in peripherals with heartbeats. The PPG signals gained some interest of researchers in the last few years, because they are used non-invasively, and they are easily captured by the emerging IoT sensors from fingertips. This paper presents a PPG-based approach to identify persons using a neural network classifier. Firstly, PPG signals are captured from a number of persons using IoT sensors. Then, unique features are extracted from captured PPG signals by estimating the Mel-Frequency Cepstral Coefficients (MFCCs). These features are fed into an Artificial Neural Network (ANN) to be trained first and used for identification of persons. A dataset of PPG signals for 35 healthy persons was collected to test the performance of the proposed approach. Experimental results demonstrate 100% and 98.07% accuracy levels using the hold-out method and the 10-fold cross-validation method, respectively.

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Cited By

Zheng QLi YPan JLi HZhou YSui CLi JLiu CYang C(2024)A Feasibility Study on Biometric Identification Using Multimodal Signal Analysis of Photoplethysmogram and PhonocardiogramProceedings of the 2024 International Conference on Smart Healthcare and Wearable Intelligent Devices10.1145/3703847.3703858(58-62)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3703847.3703858
Siam AEl-Shafai WAbou Elazm LEl-Bahnasawy NAbd El-Samie FAbou Elazm AEl-Banby G(2024)Enhanced user verification in IoT applications: a fusion-based multimodal cancelable biometric system with ECG and PPG signalsNeural Computing and Applications10.1007/s00521-023-09394-z36:12(6575-6595)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s00521-023-09394-z
Wang JMeng H(2023)Sport Fatigue Monitoring and Analyzing Through Multi-Source SensorsInternational Journal of Distributed Systems and Technologies10.4018/IJDST.31794114:2(1-11)Online publication date: 10-Feb-2023
https://dl.acm.org/doi/10.4018/IJDST.317941
Show More Cited By

Index Terms

PPG-based human identification using Mel-frequency cepstral coefficients and neural networks
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Neural networks
2. Security and privacy

Index terms have been assigned to the content through auto-classification.

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Information

Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 80, Issue 17

Jul 2021

1589 pages

ISSN:1380-7501

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2021

Accepted: 29 July 2020

Revision received: 07 July 2020

Received: 05 August 2019

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Cited By

Zheng QLi YPan JLi HZhou YSui CLi JLiu CYang C(2024)A Feasibility Study on Biometric Identification Using Multimodal Signal Analysis of Photoplethysmogram and PhonocardiogramProceedings of the 2024 International Conference on Smart Healthcare and Wearable Intelligent Devices10.1145/3703847.3703858(58-62)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3703847.3703858
Siam AEl-Shafai WAbou Elazm LEl-Bahnasawy NAbd El-Samie FAbou Elazm AEl-Banby G(2024)Enhanced user verification in IoT applications: a fusion-based multimodal cancelable biometric system with ECG and PPG signalsNeural Computing and Applications10.1007/s00521-023-09394-z36:12(6575-6595)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s00521-023-09394-z
Wang JMeng H(2023)Sport Fatigue Monitoring and Analyzing Through Multi-Source SensorsInternational Journal of Distributed Systems and Technologies10.4018/IJDST.31794114:2(1-11)Online publication date: 10-Feb-2023
https://dl.acm.org/doi/10.4018/IJDST.317941
Siam AAlmaiah MAl-Zahrani AElazm AEl Banby GEl-Shafai WEl-Samie FEl-Bahnasawy N(2021)Secure Health Monitoring Communication Systems Based on IoT and Cloud Computing for Medical Emergency ApplicationsComputational Intelligence and Neuroscience10.1155/2021/80165252021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8016525

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