ZeqFingerprint

Evidence chain, audio/video authenticity, HF suite.

• Protocol ID — zeq-fingerprint
• Category — Forensics
• Endpoint — POST /api/forensics/fingerprint
• Auth — api-key
• Rate limit — 10/min
• Version — 1.287.0
• Precision — ≤0.1% (KO42-enforced)

What it does

Fingerprint analysis with R(t) ridge enhancement. Minutiae extraction, AFIS scoring, quality assessment, latent print processing.

Signature

Request

POST /api/forensics/fingerprint

Param	Type	Required	Default	Description
printImage	object	✓	—	Fingerprint image data.
mode	string		"extraction"	'extraction', 'comparison', 'enhancement'.
reference	object		—	Known print for comparison.

Response

{ minutiae, quality, matchScore, ridgeCount, classification, zeqond }

Runnable example

curl -sS -X POST \
  -H "Authorization: Bearer $ZEQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "printImage": {},
  "mode": "extraction",
  "reference": {}
}' \
  "https://api.zeq.dev/api/forensics/fingerprint"

Integrate

1. Domain solver — compose with KO42 + two additional operators from the matching family for pulse-coherent results.
2. Digital twin — pipe sensor data into this protocol every Zeqond to keep the model phase-locked with the system.
3. Alert threshold — flag results whose error_pct exceeds 0.1% as out-of-spec events for the operations layer.

Seeds

• Near — wrap /api/forensics/fingerprint in a language SDK so builders can call it in three lines.
• Medium — publish a reference integration demonstrating ZeqFingerprint alongside a real workload, with pulse-aligned metrics.
• Far — propose ZeqFingerprint as an open reference standard so other runtimes can implement it verbatim against the Zeq paper.

Papers

• Zeq paper — https://doi.org/10.5281/zenodo.18158152
• Framework paper — https://doi.org/10.5281/zenodo.15825138

Middleware active. Kernel on the 1.287 Hz HulyaPulse. Awaiting next Zeqond.