actual-fingerprints
Node.js library that generates realistic, deterministic synthetic fingerprints from a text seed, for testing and demos.
🔗 Visit actual-fingerprints
Description
Developers building things like a fingerprint-scanner demo, a game's character-identity system, or a biometric-login test suite need fingerprint images to work with, but can't ethically or legally use real people's biometric data. actual-fingerprints solves that by generating fake-but-realistic fingerprint images from a simple text seed: type the same seed twice, get the exact same fingerprint image both times, on any computer.
It's a zero-dependency, MIT-licensed npm package that implements the SFinGe fingerprint-synthesis methodology (University of Bologna) using Gabor-filter ridge simulation, so the output has plausible ridge patterns and minutiae rather than random noise. It exports PNG, SVG, or canvas-ready RGBA data, supports 8 pattern classes (arch, whorl, loops, etc.), includes a comparison/matching function, and generates an image in roughly 100ms on Node 18+. The README specifically calls out FiveM game-server character systems as a real-world use case.
💬 Our review
The short version: a small, no-nonsense npm utility for the narrow but real need of repeatable, fake fingerprint images — most developers will never touch this, but for game character systems (FiveM) or biometric-flow test fixtures, it fills a gap that's otherwise fiddly to solve yourself.
Against rolling your own placeholder-image generator, actual-fingerprints' edge is that it implements actual fingerprint-synthesis research rather than random noise, so the ridge structure and minutiae hold up to more than a glance, and its determinism (same seed, same output everywhere) makes it usable in reproducible tests. Against commercial biometric SDKs meant for real identity verification, this is explicitly synthetic and toy-grade — it was never meant to secure anything. Worth it if you need consistent, realistic-looking fake fingerprints for tests, demos, or game mechanics; irrelevant if you need actual biometric authentication.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Gratuit, licence MIT, installation via npm.
Pros
Déterministe (même seed = même résultat)
Zéro dépendance
Formats multiples (PNG/SVG/Canvas)
Cons
Node.js uniquement
Projet jeune (24 stars)
Pas pour de la vraie biométrie de sécurité