论文标题
使用生成对抗网络的指纹合成和重建
Synthesis and Reconstruction of Fingerprints using Generative Adversarial Networks
论文作者
论文摘要
已经证明基于深度学习的模型可以提高指纹识别的准确性。尽管这些算法表现出卓越的性能，但它们需要大规模的指纹数据集进行培训和评估。在这项工作中，我们提出了一个基于StyleGAN Architecture的新颖指纹合成和重建框架，以解决与获取此类大型数据集有关的隐私问题。我们还得出了一种计算方法来修改生成的指纹的属性，同时保留其身份。这允许每个手指合成多个不同的指纹图像。特别是，我们介绍了由100K图像对组成的综合合成指纹数据集，每对与相同的身份相对应。实验表明，提出的框架表现出了指纹合成和重建的当代最先进方法。它在视觉上和欺骗基于指纹的验证系统的能力方面显着改善了生成的指纹的现实主义。代码和指纹数据集公开可用：https：//github.com/rafaelbou/fingerprint_generator。
Deep learning-based models have been shown to improve the accuracy of fingerprint recognition. While these algorithms show exceptional performance, they require large-scale fingerprint datasets for training and evaluation. In this work, we propose a novel fingerprint synthesis and reconstruction framework based on the StyleGan2 architecture, to address the privacy issues related to the acquisition of such large-scale datasets. We also derive a computational approach to modify the attributes of the generated fingerprint while preserving their identity. This allows synthesizing multiple different fingerprint images per finger. In particular, we introduce the SynFing synthetic fingerprints dataset consisting of 100K image pairs, each pair corresponding to the same identity. The proposed framework was experimentally shown to outperform contemporary state-of-the-art approaches for both fingerprint synthesis and reconstruction. It significantly improved the realism of the generated fingerprints, both visually and in terms of their ability to spoof fingerprint-based verification systems. The code and fingerprints dataset are publicly available: https://github.com/rafaelbou/fingerprint_generator.
