Preprint arXiv:2201.01699 [cs.CV]; last accessed January 12, 2022.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: Protecting a fingerprint database against attackers is very vital in order to protect against false acceptance rate or false rejection rate. A key property in distinguishing fingerprint images is by exploiting the characteristics of these different types of fingerprint images. The aim of this paper is to perform the classification of fingerprint images using the Benford's law divergence values and machine learning techniques. The usage of these Ben-ford's law divergence values as features fed into the machine learning techniques has proved to be very effective and efficient in the classification of fingerprint images. The effectiveness of our proposed methodology was demonstrated on five datasets, achieving very high classification "accuracies" of 100% for the Decision Tree and CNN. However, the "Naive" Bayes, and Logistic Regression achieved "accuracies" of 95.95%, and 90.54%, respectively. These results showed that Benford's law features and machine learning techniques especially Decision Tree and CNN can be effectively applied for the classification of fingerprint images.
Bibtex:
@misc{,
title={An Investigation of "Benford's" Law Divergence and Machine Learning Techniques for "Intra-Class" Separability of Fingerprint Images},
author={Aamo Iorliam and Orgem Emmanuel and Yahaya I. Shehu},
year={2022},
eprint={2201.01699},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Reference Type: Preprint
Subject Area(s): Computer Science, Image Processing