Intra-finger Variability of Diffusion-based Latent Fingerprint Generation

2026-04-11

AI Summary

This study systematically evaluates the intra-finger variability of latent fingerprints synthesized by diffusion models, with a focus on balancing identity consistency and style diversity. To this end, the authors construct a latent fingerprint style repository encompassing seven major databases and over 40 surface types and processing techniques, and propose a semi-automated analysis framework to quantitatively assess ridge structure fidelity and minutiae completeness in generated images. Their findings reveal, for the first time, that mismatched style embeddings relative to reference images often induce global ridge hallucinations and local minutiae inconsistencies; while overall fingerprint identity is largely preserved, low-quality regions may exhibit minor spurious additions or deletions of minutiae. This work highlights the current limitations of synthetic methods in jointly optimizing diversity and consistency, offering critical insights for future improvements in generative modeling.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• intra-finger variability
• latent fingerprint generation
• diffusion model
• identity consistency
• style diversity

Innovation

• diffusion model
• latent fingerprint generation
• intra-finger variability
• style bank
• minutiae consistency