Intra-finger Variability of Diffusion-based Latent Fingerprint Generation¶
Conference: CVPR 2026 arXiv: 2604.10040 Code: None Area: Image Generation / Biometrics Keywords: fingerprint synthesis, diffusion models, latent fingerprints, identity consistency, style diversity
TL;DR¶
This paper systematically evaluates the intra-finger variability of diffusion-model-based fingerprint synthesis. By constructing a latent fingerprint style library spanning 40 surface types and 15 development techniques, it enhances generation diversity and quantifies both local and global identity inconsistencies introduced during the generation process.
Background & Motivation¶
Background: Generative AI (GANs and DDPMs) has demonstrated the capability to produce high-quality synthetic fingerprint datasets. Fingerprint synthesis typically proceeds in two stages: generating unique identities (inter-finger variability) and generating multiple variants of the same identity (intra-finger variability). The second stage is particularly critical for latent fingerprint applications.
Limitations of Prior Work: (1) Existing models rely on holistic or random style transfer and cannot precisely specify forensic scenarios (e.g., "a latent fingerprint lifted from a glass bottle and developed with fluorescent powder"); (2) the stochasticity of the generation process may alter ridge structures and minutiae, compromising identity authenticity.
Key Challenge: An inherent tension exists between diversity and identity preservation — increasing style diversity may introduce greater identity inconsistency.
Goal: (1) Enhance style diversity in latent fingerprint generation; (2) rigorously quantify identity preservation capability.
Key Insight: A style library of 28,000 real latent fingerprints is constructed to enable precise style control, and a semi-automated framework is designed to evaluate identity consistency.
Core Idea: The style library enables controllable generation across 40+ latent fingerprint styles, while also revealing local inconsistencies in low-quality regions and global inconsistencies arising from style–reference mismatches within diffusion models.
Method¶
Overall Architecture¶
An extension of the second stage of the GenPrint framework: (1) a style library of 28,000 real latent fingerprints is curated from 7 datasets, covering 40 surface types and 15 development techniques; (2) style embeddings are used to precisely control the style of generated latent fingerprints; (3) a semi-automated framework compares minutiae and ridge patterns between reference and generated images.
Key Designs¶
-
Latent Fingerprint Style Library:
- Function: Enables precise style control for latent fingerprint generation.
- Mechanism: 28,000 real latent fingerprints are curated from 7 diverse datasets and categorized into 40 surface types (glass, metal, paper, etc.) and 15 development techniques (powder, chemical, optical, etc.), yielding 40+ discrete styles. Style embeddings are extracted for each category and used as conditional inputs to GenPrint.
- Design Motivation: The original GenPrint model is limited to generic or randomly styled latent fingerprints and cannot generate fingerprints corresponding to specific forensic scenarios.
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Semi-Automated Identity Consistency Evaluation Framework:
- Function: Quantifies minutiae and ridge changes introduced during the generation process.
- Mechanism: Synthetic versions are generated from manually annotated reference fingerprints and then aligned for comparison along two dimensions: (1) local inconsistencies — addition and removal of minutiae (detected automatically and verified manually); (2) global inconsistencies — hallucinated ridge patterns absent from the reference image.
- Design Motivation: Conventional automated matching scores lack sufficient granularity; minutiae-level analysis is required to understand the specific mechanisms through which identity is corrupted.
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Root Cause Analysis of Inconsistencies:
- Function: Explains the origin of identity inconsistencies.
- Mechanism: Local inconsistencies primarily occur in regions where the reference image is of low quality — the model tends to "fabricate" details under uncertainty. Global inconsistencies arise when the reference image does not match the selected style embedding, causing the model to hallucinate non-existent ridge patterns.
- Design Motivation: Understanding why inconsistencies occur is a prerequisite for guiding model improvement.
Loss & Training¶
The method builds upon GenPrint's ID-Net (a fine-tuned ControlNet), conditioned on style embeddings and text prompts. The training strategy follows the GenPrint framework without modification.
Key Experimental Results¶
Main Results¶
| Evaluation Dimension | Result |
|---|---|
| Style Coverage | 40 surface types × 15 development techniques |
| Data Scale | 28,000 real latent fingerprints |
| Identity Preservation | Preserved in most cases; minor local inconsistencies observed |
| Global Hallucination | Occurs under style–reference mismatch conditions |
Ablation Study¶
| Condition | Local Inconsistency | Global Inconsistency | Notes |
|---|---|---|---|
| High-quality reference | Low | Low | Best-case scenario |
| Low-quality reference | High | Low | Poor-quality regions induce minutiae changes |
| Style mismatch | Low | High | Mismatch between reference and style embedding |
Key Findings¶
- The generation process preserves identity in most cases, but low-quality regions are more prone to local inconsistencies.
- Mismatch between style embeddings and reference images is the primary cause of global hallucinations.
- These findings provide clear directions for improving synthetic fingerprint generators.
Highlights & Insights¶
- Systematic Identity Consistency Analysis: This work is the first to quantify identity preservation in diffusion-model-based fingerprint generation at the minutiae level.
- Forensic Scenario Controllability: The style library spanning 40 surface types × 15 development techniques endows latent fingerprint generation with practical forensic training value.
- Actionable Root Cause Analysis: The two identified root causes — low-quality reference regions and style–reference mismatch — directly inform model improvement strategies.
Limitations & Future Work¶
- The semi-automated evaluation framework still requires human involvement and cannot be fully automated.
- Although broad, the style library's coverage may remain incomplete.
- The paper provides analysis only and does not propose methods to resolve the identified inconsistencies.
Related Work & Insights¶
- vs. Wyzykowski et al.: Supports only 3 coarse-grained styles (good/bad/ugly), whereas this work achieves 40+ fine-grained styles.
- vs. Joshi et al.: Employs neural style transfer but lacks style control; this work achieves precise control through the style library.
Rating¶
- Novelty: ⭐⭐⭐ The style library construction is valuable, though methodological innovation is limited.
- Experimental Thoroughness: ⭐⭐⭐⭐ The identity consistency analysis is thorough and rigorous.
- Writing Quality: ⭐⭐⭐⭐ Problem formulation is clear and well-structured.
- Value: ⭐⭐⭐ Offers direct practical value to the forensic and fingerprint recognition communities.