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.

提出了一种基于生成对抗网络和一次学习技术 (FIGO) 的指纹识别方法，该方法包含指纹增强和指纹识别两个部分。首先，使用 Pix2Pix 模型直接将低质量指纹图像转换为更高水平的指纹图像，提高了指纹识别模型的性能。其次，使用一次学习方法构建了完全自动化的指纹特征提取模型，从而实现了指纹的自动化识别。