An Investigation of "Benford's" Law Divergence and Machine Learning Techniques for "Intra-Class" Separability of Fingerprint Images

TL;DR

This paper explores the use of Benford's law divergence features combined with machine learning techniques to classify fingerprint images with high accuracy, demonstrating the effectiveness of the approach across multiple datasets.

Contribution

It introduces a novel feature extraction method using Benford's law divergence for fingerprint classification and evaluates its effectiveness with various machine learning models.

Findings

Decision Tree and CNN achieved 100% accuracy.

Naive Bayes and Logistic Regression achieved over 90% accuracy.

Method proved effective across five different datasets.

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 Ben-ford'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 Ben-ford's law features and machine learning techniques especially Decision Tree and CNN can be effectively applied for the classification of fingerprint images.