#1784 add random fingerprint generator

[asolntsev]

Usage:

• faker.fingerprint().png()
• faker.fingerprint().png(300, 200, ARCH)
• faker.fingerprint().base64()
• faker.fingerprint().base64(800, 800, LOOP)

This is the first time when I tried to create PR with the help of AI (Claude).
I tuned the code format, added tests etc., but didn't dive deep into the algorithm. ¯¯_(ツ)_/¯¯

@mferretti Don't you want to try this version?

[what-the-diff]

PR Summary

• Added Access to New Fingerprint Feature
  A new function was added in the existing code which enables access to a new feature related to fingerprints.

• New Fingerprint Image Generation Capability
  A new class was introduced that allows the creation of synthetic fingerprint images. It includes tools for making PNG images and for creating data URLs.

• Distinct Fingerprint Patterns Classification
  We introduced a classification that classifies fingerprint patterns into three types: WHORL, LOOP, and ARCH.

• Customizable Generation of Fingerprint Images
  Functions were constructed to allow the generation of fingerprint images based on user-specified dimensions and pattern types.

• Testing of New Fingerprint Feature
  We created a new process of testing to ensure that the new fingerprint image generation works as intended, including checks on the properties of the PNG images.

[codecov-commenter]

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Codecov Report

❌ Patch coverage is 97.05882% with 2 lines in your changes missing coverage. Please review.
✅ Project coverage is 92.35%. Comparing base (47f4512) to head (2dc65dc).
⚠️ Report is 2 commits behind head on main.

Files with missing lines	Patch %	Lines
...java/net/datafaker/providers/base/Fingerprint.java	97.01%	2 Missing ⚠️
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Additional details and impacted files

@@             Coverage Diff              @@
##               main    #1789      +/-   ##
============================================
+ Coverage     92.28%   92.35%   +0.06%     
- Complexity     3445     3479      +34     
============================================
  Files           339      340       +1     
  Lines          6794     6881      +87     
  Branches        670      678       +8     
============================================
+ Hits           6270     6355      +85     
- Misses          356      361       +5     
+ Partials        168      165       -3     

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[Copilot]

Pull request overview

Adds a new Fingerprint base provider to generate synthetic fingerprint images as PNG bytes or base64 PNG data URLs, and wires it into the Faker provider surface and documentation.

Changes:

• Introduces net.datafaker.providers.base.Fingerprint with png(...) and base64(...) APIs (including pattern selection via PatternType).
• Registers the provider via BaseProviders#fingerprint() and updates GraalVM native-image reachability metadata.
• Adds provider documentation entries and a JUnit test suite for basic PNG/base64 validation.

Reviewed changes

Copilot reviewed 6 out of 6 changed files in this pull request and generated 6 comments.

Show a summary per file

File	Description
src/main/java/net/datafaker/providers/base/Fingerprint.java	New provider implementation for generating fingerprint images and encoding as PNG / base64 data URLs
src/main/java/net/datafaker/providers/base/BaseProviders.java	Exposes the new provider via fingerprint()
src/main/resources/META-INF/native-image/reachability-metadata.json	Adds reachability entries for the new provider and its methods
src/test/java/net/datafaker/providers/base/FingerprintTest.java	Adds tests for PNG signature/dimensions and base64 data URL behavior
README.md	Adds “Fingerprint” to the provider list
docs/documentation/providers.md	Adds the Fingerprint provider to the provider table

[mferretti]

@asolntsev
thanks for taking the time — the noise adjustments are a nice touch. That said, this is essentially the first approach I explored as well, and it hits the same fundamental limitation: with a single singularity point, the output doesn't convincingly resemble a real fingerprint.
The key insight from the research — particularly from the BioLab group at the University of Bologna's SFinGe generator — is that realistic synthetic fingerprints need multiple singularity points (cores and deltas) driving a directional map. The flow of ridges around these points is what produces the characteristic arches, loops, and whorls. A single core gives you something that looks more like a bullseye than a real print.
The reference worth looking at is SFinGe by Cappelli et al. — their four-step pipeline (directional map → density map → ridge pattern → noising) is the gold standard, but most of it can be simplified for our purposes.
Specifically: the density map can be reduced to a constant ridge frequency, the rendering to a simple grayscale threshold plus mild noise. What actually matters is the directional map and the ridge generation — and the latter doesn't need real Gabor convolution. You can compute it analytically per pixel as cos(2π · (x·cosθ + y·sinθ) / λ), where θ(x,y) comes from the directional map and λ is your (constant) ridge wavelength. That's O(width × height), no convolution, fast in Java.
With 2–4 cores/deltas placed randomly within plausible regions you get real fingerprint topology at minimal computational cost — and that's exactly what I'm currently banging my head on 🙂 (given the limited time i have after work+family). Happy to compare notes.

[bodiam]

@mferrettmate are you using AI to write your messages? If so, could you please not do that? Thx