Herdmark Biometric identity register
API up Embedder DINOv2 Gallery 88
Step 5 of 8 · Animal record

ae33f6f9…

Active Vet witnessed 8 templates

Four different businesses have money riding on this one animal at the same time, and each of them needs a different slice of the same truth. The owner wants provenance, the insurer wants the death event, the bank wants to know the collateral still exists and has not moved, the state vet wants the disease status. One record, four legitimate readers.

Real muzzle photographs · no accuracy claim is being made
The images on this screen are real cattle muzzle photographs from a public research dataset — US feedlot cattle, photographed in a single session. They are not South African breeds and none is a Cape buffalo. No score shown anywhere in this console is evidence of real-world accuracy.
Imagery Xiong, Yijie; Li, Guoming; Erickson, Galen (2022). Beef Cattle Muzzle/Noseprint database for individual identification [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.6324361. Licensed under CC BY 4.0https://creativecommons.org/licenses/by/4.0/.
Read the full disclosure

The gallery holds 12 animals and 168 frames. Twelve animals is a demonstration, not a register. Accuracy on twelve animals says nothing about accuracy on a hundred thousand: the chance of a stranger resembling someone already enrolled grows with the size of the register, and this gallery is far too small to show where that starts to bite.

The imagery is single-session. Every frame of an animal was taken on one occasion. That includes the "second capture" used on the duplicate-check screen — it is a different part of the same session, not a return visit. So this console can show that the duplicate block fires; it cannot show that a muzzle still matches weeks or months later. Whether a muzzle pattern is durable over time is the question that decides whether this product works, and no public dataset we are permitted to use commercially can answer it. It needs a longitudinal capture: the same South African animals photographed twice, weeks apart.

The population is wrong for this market. US Midwest feedyard beef yearlings; Angus, Angus x Hereford, Continental x British cross. No Bonsmara, no Nguni, no Afrikaner, no Cape buffalo. A Cape buffalo muzzle has not been put through this pipeline at all.

What the pipeline underneath does show is real, and it is the part worth watching: capture, quality gate, DINOv2 embedding, vector search, thresholding and the hash-chained audit record all run exactly as they would in production, on real photographs, with the thresholds read from the database at request time.

Where this console cannot determine something it says unknown. It never reports "real" on the strength of a guess.

Identity

Animal identifierae33f6f9-9a70-5a55-87b0-4e23027460c8
SpeciesCattle (B. taurus)
Breed
SexUnknown
Date of birth
Colour and markingsnot recorded - muzzle photograph from a public research dataset
Status Active derived from the event stream, not set directly
AssuranceVet witnessed
LITS tag
HoldingGrootfontein, Reitz district
Enrolled2026-09-08 11:52 UTC
Enrolling operator Dr Annelie Kruger Registered vet SAVC D12/34567

Interests

Not a single "owner" column. An animal can be owned by one party, insured by another, pledged to a third and physically held by a fourth, all at once — and this table is what tells you who must be notified when something happens to it.

RolePartyShareFromDetail
Owner Willem Bezuidenhout 100.0% 2026-09-08

Event history

The animal's status is derived from this stream rather than edited in place, so the causal chain an insurer needs — death, claim, carcass verification, policy termination, lien release — is reconstructable rather than overwritten.

OccurredEventRecorded byPayload
2026-09-08 11:52 UTC Enrolled 33333333-0000-4000-8000-000000000001 {"assurance": "vet_witnessed", "capture_event_id": "5b9082b1-779f-40e7-ad7d-07384df90fe2", "dedup_best_score": 0.864184, "gallery_size": 6, "model_version": "1.0", "policy_version": "v3-unl-real-perframe"}

Biometric templates

TemplateModalityModel versionDimQualityEnrolledStatus
f40b0a21… Muzzle 1.0 0.8734 2026-09-08 Active
5894a7c7… Muzzle 1.0 0.8737 2026-09-08 Active
236bb79b… Muzzle 1.0 0.8736 2026-09-08 Active
75f5de44… Muzzle 1.0 0.8723 2026-09-08 Active
1dcfd026… Muzzle 1.0 0.8704 2026-09-08 Active
7abea1c9… Muzzle 1.0 0.8693 2026-09-08 Active
dd6298f4… Muzzle 1.0 0.8664 2026-09-08 Active
9be44cb0… Muzzle 1.0 0.8668 2026-09-08 Active

Vectors are stored as 768-dimensional half-precision vectors; where the dimension column reads — the API did not return it on the row, not that it is absent from the store. Every template carries a resolvable model version. When the model is retrained, old templates are not silently reinterpreted under the new one — they are re-extracted from the retained source images, which is the reason those images are never deleted.

Capture metadata

Who captured it, where, when, and on what device. This is the evidentiary layer: if an operator's accreditation is revoked next year, every animal they enrolled is one query away.

Operator Dr Annelie Kruger Registered vet SAVC D12/34567
Captured at2026-09-08 11:52 UTC
Submitted at2026-09-08 11:52 UTC the capture-to-submit delta is itself an anti-fraud signal
GPS-27.80140, 28.43060
Frames in burst8
Devicedemo-gallery-loader
Device attestationnot reported
Capture typeVideo burst
Quality gate passed 0.8707overall Per-signal sub-scores (parallax, torch response, presentation-attack) are computed by the quality gate but are not returned by GET /v1/animals/{id} in this build — screen 2 shows them live.
Imagery photograph real capture, not generated — see the disclosure above for its provenance
Capture event5b9082b1-779f-40e7-ad7d-07384df90fe2

Stored frames

Muzzle capture frame from the demo gallery
frame_00.png
Muzzle capture frame from the demo gallery
frame_01.png
Muzzle capture frame from the demo gallery
frame_02.png
Muzzle capture frame from the demo gallery
frame_03.png