Intra-finger Variability of Diffusion-based Latent Fingerprint Generation

TL;DR

This work evaluates the intra-finger variability of synthetic latent fingerprints generated by a diffusion model, highlighting the diversity, consistency, and limitations in current generative approaches.

Contribution

It introduces a latent style bank from multiple datasets to enhance style diversity and analyzes the fidelity and inconsistencies in generated fingerprints.

Findings

Generation largely preserves fingerprint identity.

Local inconsistencies like minutiae errors occur, especially in poor quality regions.

Global inconsistencies arise from style mismatches, causing hallucinated ridge patterns.

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.