Finger Type Classification with Deep Convolution Neural Networks

Yousif Al-Wajih, Waleed Hamanah, Mohammad Abido, Mohammad Abido, Mohammad Abido, Fouad Al-Sunni, Fakhraddin Alwajih

2022

Abstract

The Automated Fingerprint Identification System (AFIS) is a biometric identification methodology that uses digital imaging technology to obtain, store, and analyse fingerprint information. There has been an increased interest in fingerprint-based security systems with the rise in demand for collecting demographic data through security applications. Reliable and highly secure, these systems are used to identify people using the unique biometric information of fingerprints. In this work, a learning-based method of identifying fingerprints was investigated. Using deep learning tools, the performance of the AFIS in terms of search time and speed of matching between fingerprint databases was successfully enhanced. A convolutional neural network (CNN) model was proposed and developed to classify fingerprints and predict fingerprint types. The proposed classification system is a novel approach that classifies fingerprints based on figure type. Two public datasets were used to train and evaluate the proposed CNN model. The proposed model achieved high validation accuracy with both databases, with an overall accuracy in predicting fingerprint types at around 94%.

Paper Citation

in Harvard Style

Al-Wajih Y., Hamanah W., Abido M., Al-Sunni F. and Alwajih F. (2022). Finger Type Classification with Deep Convolution Neural Networks. In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-585-2, pages 247-254. DOI: 10.5220/0011327100003271

in Bibtex Style

@conference{icinco22,
author={Yousif Al-Wajih and Waleed Hamanah and Mohammad Abido and Fouad Al-Sunni and Fakhraddin Alwajih},
title={Finger Type Classification with Deep Convolution Neural Networks},
booktitle={Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2022},
pages={247-254},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011327100003271},
isbn={978-989-758-585-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Finger Type Classification with Deep Convolution Neural Networks
SN - 978-989-758-585-2
AU - Al-Wajih Y.
AU - Hamanah W.
AU - Abido M.
AU - Al-Sunni F.
AU - Alwajih F.
PY - 2022
SP - 247
EP - 254
DO - 10.5220/0011327100003271