This study explores the generation of synthesized fingerprint images using
Denoising Diffusion Probabilistic Models (DDPMs). The significant obstacles in
collecting real biometric data, such as privacy concerns and the demand for
diverse datasets, underscore the imperative for synthetic biometric
alternatives that are both realistic and varied. Despite the strides made with
Generative Adversarial Networks (GANs) in producing realistic fingerprint
images, their limitations prompt us to propose DDPMs as a promising
alternative. DDPMs are capable of generating images with increasing clarity and
realism while maintaining diversity. Our results reveal that DiffFinger not
only competes with authentic training set data in quality but also provides a
richer set of biometric data, reflecting true-to-life variability. These
findings mark a promising stride in biometric synthesis, showcasing the
potential of DDPMs to advance the landscape of fingerprint identification and
authentication systems.

该研究探索了使用去噪扩散概率模型（DDPMs）生成合成指纹图像。相对于收集真实生物识别数据所面临的隐私和多样性数据需求困难，本研究旨在提出 DDPMs 作为一种既真实又多样的合成生物识别替代方案。结果显示，DiffFinger 不仅在质量上能与真实训练集数据相媲美，还提供了更丰富的生物识别数据，反映出真实生活中的变异。这些发现标志着生物识别合成领域的一个重要进展，并展示了 DDPMs 在指纹识别和认证系统领域的潜力。