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.

翻译：指纹证据在刑事调查中对个体身份识别具有重要作用。尽管已有多种技术被提出用于指纹分类与特征提取，自动化指纹识别仍处于早期发展阶段。传统自动指纹识别系统（AFIS）的性能依赖于有效细节点的存在，并在特征提取与识别阶段仍需人类专家辅助。基于此动机，我们提出了一种基于生成对抗网络与一次性学习技术的指纹识别方法（FIGO）。该方案包含两个组成部分：指纹增强层与指纹识别层。首先，在指纹增强层中，我们提出了一种Pix2Pix模型，通过逐像素变换直接将低质量指纹图像提升至更高质量层级。通过所提出的增强算法，指纹识别模型的性能得到显著提升。此外，我们基于Gabor滤波器开发了另一种现有解决方案作为基准，通过观察指纹设备的识别精度与所提模型进行对比。实验结果表明，所提出的Pix2Pix模型在指纹识别方面比基线方法具有更优的支撑能力。其次，我们采用一次性学习方法构建了全自动指纹特征提取模型，用于在指纹识别过程中区分不同指纹。该过程使用两个共享权重与参数的双胞胎卷积神经网络（CNN）获取特征向量。通过所提方法，我们证明仅需单个训练样本即可高精度学习必要信息。