论文标题
FIGO：使用GAN和一击学习技术增强的指纹识别方法
FIGO: Enhanced Fingerprint Identification Approach Using GAN and One Shot Learning Techniques
论文作者
论文摘要
指纹证据在识别个人的刑事调查中起着重要作用。尽管已经提出了各种指纹分类和特征提取的技术，但指纹的自动指纹识别仍处于最早的阶段。传统\ textIt {自动指纹识别系统}（AFIS）的性能取决于有效的小小的点，并且仍然需要人类的专家帮助在功能提取和识别阶段。基于这种动机，我们提出了一种基于生成对抗网络和一声学习技术（FIGO）的指纹识别方法。我们的解决方案包含两个组成部分：指纹增强层和指纹识别层。首先，我们提出了一个PIX2PIX模型，将低质量的指纹图像转换为直接在指纹增强层中的Pixel的指纹图像像素的更高水平。通过提出的增强算法，指纹识别模型的性能得到显着提高。此外，我们通过观察指纹设备的识别准确性来开发基于Gabor过滤器的另一种现有解决方案，作为与提议模型进行比较的基准。实验结果表明，我们提出的PIX2PIX模型比指纹识别的基线方法具有更好的支持。其次，我们使用一种单次学习方法构建一个完全自动化的指纹特征提取模型，以在指纹识别过程中区分每个指纹与其他指纹。两个具有共享权重和参数的双卷积神经网络（CNN）用于在此过程中获得特征向量。使用所提出的方法，我们证明只能以高准确性从一个培训样本中学习必要的信息。
Fingerprint evidence plays an important role in a criminal investigation for the identification of individuals. Although various techniques have been proposed for fingerprint classification and feature extraction, automated fingerprint identification of fingerprints is still in its earliest stage. The performance of traditional \textit{Automatic Fingerprint Identification System} (AFIS) depends on the presence of valid minutiae points and still requires human expert assistance in feature extraction and identification stages. Based on this motivation, we propose a Fingerprint Identification approach based on Generative adversarial network and One-shot learning techniques (FIGO). Our solution contains two components: fingerprint enhancement tier and fingerprint identification tier. First, we propose a Pix2Pix model to transform low-quality fingerprint images to a higher level of fingerprint images pixel by pixel directly in the fingerprint enhancement tier. With the proposed enhancement algorithm, the fingerprint identification model's performance is significantly improved. Furthermore, we develop another existing solution based on Gabor filters as a benchmark to compare with the proposed model by observing the fingerprint device's recognition accuracy. Experimental results show that our proposed Pix2pix model has better support than the baseline approach for fingerprint identification. Second, we construct a fully automated fingerprint feature extraction model using a one-shot learning approach to differentiate each fingerprint from the others in the fingerprint identification process. Two twin convolutional neural networks (CNNs) with shared weights and parameters are used to obtain the feature vectors in this process. Using the proposed method, we demonstrate that it is possible to learn necessary information from only one training sample with high accuracy.
