DiffFinger：通过去噪扩散概率模型推进合成指纹生成
DiffFinger: Advancing Synthetic Fingerprint Generation through Denoising Diffusion Probabilistic Models
AI总结：
针对真实指纹数据采集的隐私与多样性难题，本文提出DiffFinger，利用DDPM生成高质量、多样且逼真的合成指纹，效果可媲美真实训练数据。
摘要：
本研究探索使用去噪扩散概率模型（Denoising Diffusion Probabilistic Models，DDPMs）生成合成指纹图像。收集真实生物特征数据面临重大障碍，例如隐私问题以及对多样化数据集的需求，这些都凸显了开发现实且多样的合成生物特征替代方案的必要性。尽管生成对抗网络（Generative Adversarial Networks，GANs）在生成逼真指纹图像方面已取得进展，但其局限性促使我们提出将DDPMs作为一种有前景的替代方案。DDPMs能够在保持多样性的同时，生成清晰度和真实感不断提升的图像。我们的结果表明，DiffFinger不仅在质量上可与真实训练集数据相媲美，还提供了更丰富的生物特征数据集，反映出贴近现实的变异性。这些发现标志着生物特征合成领域迈出了有前景的一步，展示了DDPMs推动指纹识别与认证系统发展的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
