The utilization of synthetic data for fingerprint recognition has garnered increased attention due to its potential to alleviate privacy concerns surrounding sensitive biometric data. However, current methods for generating fingerprints have limitations in creating impressions of the same finger with useful intra-class variations. To tackle this challenge, we present GenPrint, a framework to produce fingerprint images of various types while maintaining identity and offering humanly understandable control over different appearance factors such as fingerprint class, acquisition type, sensor device, and quality level. Unlike previous fingerprint generation approaches, GenPrint is not confined to replicating style characteristics from the training dataset alone: it enables the generation of novel styles from unseen devices without requiring additional fine-tuning. To accomplish these objectives, we developed GenPrint using latent diffusion models with multimodal conditions (text and image) for consistent generation of style and identity. Our experiments leverage a variety of publicly available datasets for training and evaluation. Results demonstrate the benefits of GenPrint in terms of identity preservation, explainable control, and universality of generated images. Importantly, the GenPrint-generated images yield comparable or even superior accuracy to models trained solely on real data and further enhances performance when augmenting the diversity of existing real fingerprint datasets.

翻译：利用合成数据进行指纹识别已获得广泛关注，因其具有缓解敏感生物特征数据隐私问题的潜力。然而，当前指纹生成方法在创建同一手指具有有效类内差异的指纹图像方面存在局限性。为应对这一挑战，我们提出GenPrint框架，该框架能够生成多种类型的指纹图像，同时保持身份一致性，并对指纹类别、采集类型、传感器设备及质量水平等不同外观因素提供人类可理解的控制。与既往指纹生成方法不同，GenPrint不局限于仅复制训练数据集中的风格特征：它能够在无需额外微调的情况下，从未见设备生成全新风格。为实现上述目标，我们基于多模态条件（文本与图像）的潜扩散模型开发了GenPrint，以实现风格与身份的一致性生成。实验采用多种公开数据集进行训练与评估。结果表明，GenPrint在身份保持、可解释控制及生成图像通用性方面具有优势。重要的是，GenPrint生成的图像在模型训练中可达到与纯真实数据训练模型相当甚至更优的准确性，并在增强现有真实指纹数据集多样性时进一步提升性能。