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arxiv: 2604.10040 · v1 · submitted 2026-04-11 · 💻 cs.CV

Intra-finger Variability of Diffusion-based Latent Fingerprint Generation

Noor Hussein , Anil K. Jain , Karthik Nandakumar This is my paper

Pith reviewed 2026-05-10 15:28 UTC · model grok-4.3

classification 💻 cs.CV
keywords latent fingerprintsdiffusion modelssynthetic generationminutiae consistencyridge integrityintra-finger variabilitystyle embeddingidentity preservation
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The pith

Diffusion models for latent fingerprints preserve identity but add minutiae inconsistencies and hallucinate ridges when styles mismatch.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests how much a state-of-the-art diffusion model changes the same finger across multiple synthetic latent print generations. It first builds a latent style bank drawn from seven datasets to produce prints in more than 40 distinct styles that reflect different surfaces and processing methods. A semi-automated framework then measures ridge and minutiae integrity and shows that identity is largely kept intact. Small numbers of local errors appear as added or removed minutiae points, especially in low-quality regions of the input image, while mismatches between the reference and the chosen style embedding produce global errors such as entirely hallucinated ridge patterns. The results indicate that current generators cannot yet deliver both high style diversity and reliable identity consistency at the same time.

Core claim

Although the diffusion-based generation process largely preserves finger identity, it introduces a small number of local inconsistencies in the form of added and removed minutiae, particularly when the reference image contains poor-quality regions. In addition, a mismatch between the reference image and the selected style embedding produces global inconsistencies visible as hallucinated ridge patterns. These observations are obtained through a semi-automated framework that evaluates ridge and minutiae integrity on impressions synthesized from a comprehensive latent style bank curated from seven diverse datasets and covering over 40 distinct styles.

What carries the argument

The semi-automated framework that quantifies the integrity of ridges and minutiae by detecting local additions or removals and global hallucinated patterns in the generated impressions.

If this is right

  • Existing diffusion-based generators cannot simultaneously achieve high style diversity and full identity consistency.
  • Poor-quality regions in reference images directly increase the rate of minutiae addition and removal.
  • Style embedding mismatches reliably produce hallucinated ridge patterns that do not correspond to the reference.
  • Further model improvements are required to reduce both local minutiae errors and global ridge hallucinations.
  • A larger latent style bank increases style coverage but does not remove the underlying consistency limitations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Training fingerprint matchers exclusively on outputs from these generators may embed the same local and global inconsistencies into downstream recognition performance.
  • Adding an explicit quality map or consistency regularizer during generation could reduce the observed minutiae and ridge errors.
  • Running commercial matchers on pairs of generated prints from the same finger would quantify whether the reported inconsistencies affect actual matching scores.
  • The same analysis applied to non-diffusion generative models would reveal whether the inconsistencies are specific to the diffusion process or common across synthesis methods.

Load-bearing premise

The semi-automated framework for assessing ridge and minutiae integrity produces reliable measurements that accurately reflect true inconsistencies without its own detection errors or biases.

What would settle it

If manual minutiae annotations by multiple forensic experts on the same set of generated prints disagree substantially with the locations and counts reported by the semi-automated framework, the framework's measurements would be shown to be unreliable.

Figures

Figures reproduced from arXiv: 2604.10040 by Anil K. Jain, Karthik Nandakumar, Noor Hussein.

Figure 1
Figure 1. Figure 1: Example of a synthetic latent print. The [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An illustration of core tasks involved in fingerprint synthesis and how they are handled by the GenPrint framework [ [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Our proposed modification of GenPrint Stage 2 for di [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Examples of real latent print (left) and synthetic latent print (right) with different latent styles. Here, the style embedding from [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visual comparison of two real latent prints developed on [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Detailed overview of the finger placement simulator that provides ridge pattern guidance. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Genuine and impostor match score distributions for real [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The average NFIQ2 quality for synthetic vs. real latent prints. The circle size is proportional to the size of the latent style bank [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Scatter plot correlating the average LFIQA scores of [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Examples of NFIQ2 quality score distributions for real [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Local error analysis. The first column displays the ground-truth minutiae in [PITH_FULL_IMAGE:figures/full_fig_p009_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Qualitative comparison of global hallucination across different sensor styles. The [PITH_FULL_IMAGE:figures/full_fig_p009_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Examples of extrapolation due to enhancement errors. [PITH_FULL_IMAGE:figures/full_fig_p010_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Example of global ridge pattern hallucination. [PITH_FULL_IMAGE:figures/full_fig_p010_14.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper evaluates intra-finger variability in diffusion-generated latent fingerprints. It constructs a latent style bank from seven datasets to synthesize prints across over 40 styles and applies a semi-automated framework to measure ridge and minutiae integrity. The central claim is that identity is largely preserved, with only small local inconsistencies (minutiae additions/removals) that increase in poor-quality reference regions, plus occasional global inconsistencies (hallucinated ridges) arising from reference-style mismatches.

Significance. If the measurements hold, the work usefully documents concrete limitations of current diffusion models for latent fingerprint synthesis, particularly the tension between style diversity and identity fidelity. The curation of a multi-dataset style bank is a concrete strength that enables controlled style-variation experiments and could support future benchmarking.

major comments (1)
  1. [§4] §4 (semi-automated framework description): The central observational claims—that the generator introduces only a 'small number of local inconsistencies' and occasional 'hallucinated ridge patterns'—rest entirely on counts produced by the semi-automated minutiae/ridge integrity framework. No validation against expert forensic annotations, no precision-recall figures, no inter-rater reliability statistics, and no ablation of the automated component on diffusion-generated images are reported. This is load-bearing because elevated false-positive/negative rates on low-quality or hallucinated regions would directly inflate or deflate the reported inconsistency counts.
minor comments (2)
  1. [Abstract] Abstract and §3: The claim of 'over 40 distinct styles' is stated without an explicit count or breakdown of how many styles were actually used in the reported experiments versus the full bank.
  2. [§5] §5 (results): Quantitative tables or plots lack error bars, confidence intervals, or per-image statistics on minutiae change counts, making it hard to judge the variability of the reported 'small number' of inconsistencies.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The feedback on the validation of our semi-automated framework is well-taken and will strengthen the manuscript. We address the major comment point-by-point below.

read point-by-point responses

  1. Referee: [§4] §4 (semi-automated framework description): The central observational claims—that the generator introduces only a 'small number of local inconsistencies' and occasional 'hallucinated ridge patterns'—rest entirely on counts produced by the semi-automated minutiae/ridge integrity framework. No validation against expert forensic annotations, no precision-recall figures, no inter-rater reliability statistics, and no ablation of the automated component on diffusion-generated images are reported. This is load-bearing because elevated false-positive/negative rates on low-quality or hallucinated regions would directly inflate or deflate the reported inconsistency counts.

    Authors: We acknowledge that the manuscript does not report formal validation of the semi-automated framework against expert forensic annotations, precision-recall metrics, inter-rater reliability, or an ablation study on diffusion-generated images. The framework combines established automated minutiae/ridge detectors with targeted manual review to handle the challenges of latent prints, but this does not substitute for quantitative validation. We agree this is a substantive limitation that could affect interpretation of the reported inconsistency counts. In the revised manuscript, we will expand Section 4 to include: (1) precision, recall, and F1 scores obtained by comparing framework outputs to annotations from a certified forensic expert on a held-out subset of 200 generated images; (2) inter-rater reliability statistics (Cohen's kappa) between the framework and the expert; and (3) an ablation isolating the automated components evaluated directly on diffusion-generated latents. These additions will be placed in the main text with supporting details in the supplement. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical evaluation with independent measurements

full rationale

This paper conducts an empirical study comparing diffusion-generated latent fingerprints against reference images using a semi-automated ridge/minutiae integrity framework built from standard extractors and detectors. No derivations, predictions, or equations are claimed; the central observations (identity preservation with localized inconsistencies) rest on direct output-to-reference comparisons rather than any fitted parameter renamed as a result or self-citation chain. The style bank curation and framework implementation are described as procedural steps without reducing the reported inconsistency counts to the inputs by construction. External benchmarks (existing minutiae detectors) provide independent grounding, satisfying the self-contained criterion.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work relies on standard assumptions from generative modeling and fingerprint biometrics without introducing new free parameters, axioms beyond domain norms, or invented entities.

axioms (2)
  • domain assumption Diffusion models conditioned on style embeddings can produce realistic latent fingerprint impressions
    Implicit in the use of the state-of-the-art diffusion model for generation
  • domain assumption Minutiae and ridge integrity can be assessed via semi-automated detection without significant false positives or negatives
    Required for the integrity analysis framework described

pith-pipeline@v0.9.0 · 5479 in / 1324 out tokens · 47239 ms · 2026-05-10T15:28:59.126514+00:00 · methodology

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Reference graph

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