An Investigation of "Benford's" Law Divergence and Machine Learning Techniques for "Intra-Class" Separability of Fingerprint Images
This work addresses fingerprint image classification for biometric security, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of classifying fingerprint images to protect databases from attackers by using Benford's law divergence values as features with machine learning techniques, achieving up to 100% accuracy with Decision Tree and CNN on five datasets.
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.