指纹图像“类内”可分性的“Benford”定律发散性和机器学习技术研究
An Investigation of "Benford's" Law Divergence and Machine Learning Techniques for "Intra-Class" Separability of Fingerprint Images
摘要：
保护指纹数据库免受攻击者的攻击是非常重要的，以防止错误的接受率或错误的拒绝率。区分指纹图像的一个关键属性是利用这些不同类型的指纹图像的特性。本文的目的是进行分类的指纹图像使用本福特定律的分歧值和机器学习技术。使用这些本福特定律的分歧值作为特征送入机器学习技术已被证明是非常有效和高效的指纹图像的分类。我们提出的方法的有效性在五个数据集上得到了证明，决策树和CNN的分类“准确率”达到了100%。然而，“朴素”贝叶斯和逻辑回归分别达到了95.95%和90.54%的“准确率”。实验结果表明，本福特定律特征和机器学习技术，特别是决策树和CNN可以有效地应用于指纹图像的分类。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
