Fingerprint Large Classification Using Sequential Learning on Parallel Environment

Nicolás Reyes-Reyes, Marcela González-Araya, Wladimir Soto-Silva

2024

Abstract

Fingerprint classification allows a biometric identification system to reduce search space in databases and therefore response times. In the literature, fingerprint classification has been addressed through different approaches where deep learning techniques such as convolutional neural networks have been gaining attention. However, the proposed approaches use extremely small data sets for large-scale real-world scenarios that could worsen accuracy rates due to interclass and intraclass variations in fingerprints. For this reason, we proposed a fingerprint classification approach that allows us to address this problem by considering millions of samples. For this purpose, a classifier based on neural networks trained using online sequential extreme learning machines was developed. Likewise, to accelerate the training of the classifier, the matrix operations inside it was run in a graphic processing unit. In order to evaluate our proposal, the approach was tested on three datasets with more than two million synthetic fingerprint image descriptors. The results are similar in terms of accuracy and computational time to recent approaches but using more than 2.5 million samples.

Paper Citation

in Harvard Style

Reyes-Reyes N., González-Araya M. and Soto-Silva W. (2024). Fingerprint Large Classification Using Sequential Learning on Parallel Environment. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 222-230. DOI: 10.5220/0012355300003636

in Bibtex Style

@conference{icaart24,
author={Nicolás Reyes-Reyes and Marcela González-Araya and Wladimir Soto-Silva},
title={Fingerprint Large Classification Using Sequential Learning on Parallel Environment},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={222-230},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012355300003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Fingerprint Large Classification Using Sequential Learning on Parallel Environment
SN - 978-989-758-680-4
AU - Reyes-Reyes N.
AU - González-Araya M.
AU - Soto-Silva W.
PY - 2024
SP - 222
EP - 230
DO - 10.5220/0012355300003636
PB - SciTePress