Training fingerprint recognition models using synthetic data has recently gained increased attention in the biometric community as it alleviates the dependency on sensitive personal data. Existing approaches for fingerprint generation are limited in their ability to generate diverse impressions of the same finger, a key property for providing effective data for training recognition models. To address this gap, we present FPGAN-Control, an identity preserving image generation framework which enables control over the fingerprint's image appearance (e.g., fingerprint type, acquisition device, pressure level) of generated fingerprints. We introduce a novel appearance loss that encourages disentanglement between the fingerprint's identity and appearance properties. In our experiments, we used the publicly available NIST SD302 (N2N) dataset for training the FPGAN-Control model. We demonstrate the merits of FPGAN-Control, both quantitatively and qualitatively, in terms of identity preservation level, degree of appearance control, and low synthetic-to-real domain gap. Finally, training recognition models using only synthetic datasets generated by FPGAN-Control lead to recognition accuracies that are on par or even surpass models trained using real data. To the best of our knowledge, this is the first work to demonstrate this.

翻译：近年来，使用合成数据训练指纹识别模型在生物特征识别领域受到越来越多关注，因为它减轻了对敏感个人数据的依赖。现有指纹生成方法在生成同一手指的不同图像时能力有限，而这正是为训练识别模型提供有效数据的关键特性。为解决这一不足，我们提出FPGAN-Control——一种身份保持的图像生成框架，可对生成指纹的图像外观（如指纹类型、采集设备、按压程度）进行控制。我们引入了一种新颖的外观损失函数，以促进指纹身份与外观属性之间的解耦。实验中，我们使用公开的NIST SD302（N2N）数据集训练FPGAN-Control模型。通过定性与定量分析，我们证明了FPGAN-Control在身份保持水平、外观控制程度以及低合成-真实域差距方面的优势。最后，仅使用FPGAN-Control生成的合成数据集训练识别模型，其识别准确率可达到甚至超越采用真实数据训练的模型。据我们所知，这是首个实现此成果的研究工作。