GAN-Based Data Augmentation for Improving Biometric Authentication Using CWT Images of Blood Flow Sounds

Natasha Sahare, Natasha Sahare, Patricio Fuentealba, Rutuja Salvi, Anja Burmann, Jasmin Henze

2024

Abstract

Biometric identification allows to secure sensitive information. Since existing biometric traits, such as finger-prings, voice, etc. are associated with different limitations, we exemplified the potential of blood flow sounds for biometric authentication in previous work. Therefore, we used measurements from seven different users acquired with a custom-built auscultation device to calculate the spectrograms of these signals for each cardiac cycle using continuous wavelet transform (CWT). The resulting spectral images were then used for training of a convolutional neural network (CNN). In this work, we repeated the same experiment with data from twelve users by adding more data from the original seven users and data from five more users. This lead to an imbalanced dataset, where the amount of available data for the new users was much smaller, e.g., U1 had more than 900 samples per side whereas the new user U9 had less than 100 samples per side. We experienced a lower performance for the new users, i.e. their sensitivity was 18-21% lower than the overall accuracy. Thus, we examined whether the augmentation of data leads to better results. This analysis was performed using generative adversarial networks (GANs). The newly generated data was then used for training of a CNN with several different settings, revealing the potential of GAN-based data augmentation for increasing the accuracy of biometric authentication using blood flow sounds.

Paper Citation

in Harvard Style

Sahare N., Fuentealba P., Salvi R., Burmann A. and Henze J. (2024). GAN-Based Data Augmentation for Improving Biometric Authentication Using CWT Images of Blood Flow Sounds. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-688-0, SciTePress, pages 340-345. DOI: 10.5220/0012318100003657

in Bibtex Style

@conference{healthinf24,
author={Natasha Sahare and Patricio Fuentealba and Rutuja Salvi and Anja Burmann and Jasmin Henze},
title={GAN-Based Data Augmentation for Improving Biometric Authentication Using CWT Images of Blood Flow Sounds},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2024},
pages={340-345},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012318100003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - GAN-Based Data Augmentation for Improving Biometric Authentication Using CWT Images of Blood Flow Sounds
SN - 978-989-758-688-0
AU - Sahare N.
AU - Fuentealba P.
AU - Salvi R.
AU - Burmann A.
AU - Henze J.
PY - 2024
SP - 340
EP - 345
DO - 10.5220/0012318100003657
PB - SciTePress