On the use of convolutional neural networks for robust classification of multiple fingerprint captures
On the use of convolutional neural networks for robust classification of multiple fingerprint captures
复制标题
DOI：
10.1002/int.21948
复制
发表时间：
2017-03
影响因子：
7
通讯作者：
Daniel Peralta;I. Triguero;S. García;Y. Saeys;J. M. Benítez;F. Herrera
中科院分区：
文献类型：
--
作者：
Daniel Peralta;I. Triguero;S. García;Y. Saeys;J. M. Benítez;F. Herrera
指纹分类是在大型指纹数据库中加速识别的最常用方法之一。指纹被分组为不相交的类，使得输入指纹仅与属于预测类的指纹进行比较，从而降低搜索的渗透率。分类过程通常从指纹图像的特征提取开始，通常基于视觉特征。在这项工作中，我们提出了一种使用卷积神经网络进行指纹分类的方法，该方法通过将图像处理纳入分类器的训练中来避免显式特征提取过程的必要性。此外，这种方法即使对于被常用算法（例如FingerCode）拒绝的低质量指纹也能够预测类别。该研究特别重视对同一指纹的不同印象的分类的鲁棒性，旨在最大限度地减少数据库中的渗透。在我们的实验中，卷积神经网络比基于显式特征提取的最先进分类器具有更好的准确性和渗透率。测试网络的运行时间也有所改善，这是特征提取和分类的联合优化的结果。
Fingerprint classification is one of the most common approaches to accelerate the identification in large databases of fingerprints. Fingerprints are grouped into disjoint classes, so that an input fingerprint is compared only with those belonging to the predicted class, reducing the penetration rate of the search. The classification procedure usually starts by the extraction of features from the fingerprint image, frequently based on visual characteristics. In this work, we propose an approach to fingerprint classification using convolutional neural networks, which avoid the necessity of an explicit feature extraction process by incorporating the image processing within the training of the classifier. Furthermore, such an approach is able to predict a class even for low‐quality fingerprints that are rejected by commonly used algorithms, such as FingerCode. The study gives special importance to the robustness of the classification for different impressions of the same fingerprint, aiming to minimize the penetration in the database. In our experiments, convolutional neural networks yielded better accuracy and penetration rate than state‐of‐the‐art classifiers based on explicit feature extraction. The tested networks also improved on the runtime, as a result of the joint optimization of both feature extraction and classification.