论文标题
对“ Benford”法律的分歧和机器学习技术的调查
An Investigation of "Benford's" Law Divergence and Machine Learning Techniques for "Intra-Class" Separability of Fingerprint Images
论文作者
论文摘要
保护指纹数据库免受攻击者的影响至关重要，以防止错误的接受率或错误的拒绝率。区分指纹图像的关键特性是利用这些不同类型的指纹图像的特征。本文的目的是使用Ben-Ford的定律差异值和机器学习技术对指纹图像进行分类。事实证明，这些Ben-ford定律的分歧值用作馈入机器学习技术的特征，在指纹图像的分类中非常有效。在五个数据集上证明了我们提出的方法的有效性，为决策树和CNN实现了非常高的分类“精度”。但是，“天真”的贝叶斯和逻辑回归的“准确性”分别为95.95％和90.54％。这些结果表明，Ben-Ford的定律特征和机器学习技术，尤其是决策树和CNN，可以有效地用于指纹图像的分类。
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.
