Contactless fingerprint recognition offers a hygienic and convenient alternative to contact-based systems, enabling rapid acquisition without latent prints, pressure artifacts, or hygiene risks. However, contactless images often show degraded ridge clarity due to illumination variation, subcutaneous skin discoloration, and specular reflections. Flash captures preserve ridge detail but introduce noise, whereas non-flash captures reduce noise but lower ridge contrast. We propose Fusion2Print (F2P), the first framework to systematically capture and fuse paired flash-non-flash contactless fingerprints. We construct a custom paired dataset, FNF Database, and perform manual flash-non-flash subtraction to isolate ridge-preserving signals. A lightweight attention-based fusion network also integrates both modalities, emphasizing informative channels and suppressing noise, and then a U-Net enhancement module produces an optimally weighted grayscale image. Finally, a deep embedding model with cross-domain compatibility, generates discriminative and robust representations in a unified embedding space compatible with both contactless and contact-based fingerprints for verification. F2P enhances ridge clarity and achieves superior recognition performance (AUC=0.999, EER=1.12%) over single-capture baselines (Verifinger, DeepPrint).

翻译：非接触式指纹识别为接触式系统提供了一种卫生且便捷的替代方案，能够实现快速采集，且无潜在指纹、压力伪影或卫生风险。然而，由于光照变化、皮下皮肤变色和镜面反射，非接触式图像常出现脊线清晰度下降的问题。闪光拍摄能保留脊线细节但会引入噪声，而非闪光拍摄能降低噪声但会降低脊线对比度。我们提出了Fusion2Print（F2P），这是首个系统性地采集并融合成对闪光-非闪光非接触式指纹的框架。我们构建了一个自定义的成对数据集FNF Database，并执行手动闪光-非闪光差分以分离出保留脊线的信号。一个轻量级的基于注意力的融合网络同时整合两种模态，强调信息丰富的通道并抑制噪声，随后一个U-Net增强模块生成最优加权的灰度图像。最后，一个具有跨域兼容性的深度嵌入模型，在与非接触式和接触式指纹均兼容的统一嵌入空间中生成具有区分性和鲁棒性的表示，用于验证。F2P增强了脊线清晰度，并相较于单次采集基线方法（Verifinger, DeepPrint）实现了更优的识别性能（AUC=0.999，EER=1.12%）。