  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.
