用扩散模型生成指纹时，同一手指的多样性与保真度存在矛盾。
Intra-finger Variability of Diffusion-based Latent Fingerprint Generation
- 构建7个数据集的潜空间风格库，生成40+种不同质感指纹。
- 参考图质量差时易出现细节点增减，风格不匹配引发全局伪影。
- 适合研究生成指纹真实性与生物特征一致性的人看。
本文系统评估了基于扩散模型生成的合成指纹（尤其是潜指纹）的同一手指内变异特性。通过整合七个不同数据集构建全面的潜空间风格库，实现对超过40种不同表面和处理方式的潜指纹精准合成。同时，设计半自动化框架分析生成指纹的脊线与细节点完整性。结果表明，尽管生成过程基本保持身份一致，但在参考图像质量较差区域仍会出现局部不一致（如细节点的增减）；当参考图像与生成引导的风格嵌入不匹配时，会引入全局性伪影脊线模式。这些发现揭示了现有合成指纹生成模型在多样性与身份一致性间的权衡，亟需进一步优化。
原文摘要 · Abstract (English)
The primary goal of this work is to systematically evaluate the intra-finger variability of synthetic fingerprints (particularly latent prints) generated using a state-of-the-art diffusion model. Specifically, we focus on enhancing the latent style diversity of the generative model by constructing a comprehensive \textit{latent style bank} curated from seven diverse datasets, which enables the precise synthesis of latent prints with over 40 distinct styles encapsulating different surfaces and processing techniques. We also implement a semi-automated framework to understand the integrity of fingerprint ridges and minutiae in the generated impressions. Our analysis indicates that though the generation process largely preserves the identity, a small number of local inconsistencies (addition and removal of minutiae) are introduced, especially when there are poor quality regions in the reference image. Furthermore, mismatch between the reference image and the chosen style embedding that guides the generation process introduces global inconsistencies in the form of hallucinated ridge patterns. These insights highlight the limitations of existing synthetic fingerprint generators and the need to further improve these models to simultaneously enhance both diversity and identity consistency.
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