论文标题
使用正常指纹数据库训练小面积指纹识别深神经网络的数据增强方法
A Method of Data Augmentation to Train a Small Area Fingerprint Recognition Deep Neural Network with a Normal Fingerprint Database
论文作者
论文摘要
由于易于获取，唯一性和可用性，指纹在基于生物识别的系统中很受欢迎。如今，它用于智能手机安全性，数字支付和数字储物柜中。基于细节的传统指纹匹配方法主要适用于大区块指纹，并且在处理智能手机的小区域指纹时，准确率将大大降低。有很多尝试使用深度学习来获得小区域指纹识别，并且有很多成功。但是培训深度神经网络需要大量的数据集进行培训。没有针对小区域的知名数据集，因此我们必须自己制作数据集。在本文中，我们提出了一种数据增强方法，以训练具有正常指纹数据库（例如FVC2002）的小区域指纹识别深神经网络，并通过测试对其进行验证。实验结果表明我们方法的效率。
Fingerprints are popular among the biometric based systems due to ease of acquisition, uniqueness and availability. Nowadays it is used in smart phone security, digital payment and digital locker. The traditional fingerprint matching methods based on minutiae are mainly applicable for large-area fingerprint and the accuracy rate would reduce significantly when dealing with small-area fingerprint from smart phone. There are many attempts to using deep learning for small-area fingerprint recognition, and there are many successes. But training deep neural network needs a lot of datasets for training. There is no well-known dataset for small-area, so we have to make datasets ourselves. In this paper, we propose a method of data augmentation to train a small-area fingerprint recognition deep neural network with a normal fingerprint database (such as FVC2002) and verify it via tests. The experimental results showed the efficiency of our method.
