Menu

Individual skill

face-matching

0upvotes

Diagnose, improve, or operate person-centered face matching across photo libraries, video, social profiles, directories, events, and other media when reference discovery, identity verification, detection failures, unknown people, or safe face-to-person assignment are involved. Use for face recognition and face-reference sourcing; not for generic image editing, image generation, or galleries without an identity-matching problem.

face-matching/SKILL.md

Read pinned source

Source commit 1acf58f08cca24b764a4352a6c30fed1f7fb9fb6

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
No
Repository scan complete
No
Skill scan complete
No
Observed behavior
Not checked yet
Last checked
Nov 7, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
194
Repository last updated
Nov 3, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# Face Matching

Match real faces to the right people while preserving uncertainty, consent, source boundaries, and existing correct assignments. Keep the ordinary path simple for people who already have a good reference; spend additional retrieval and inference only on unresolved cases.

Read supporting material only when it helps the task:

- [references/resources-and-source-strategies.md](references/resources-and-source-strategies.md): inventory of usable person records, LinkedIn/X/GitHub, employer/personal pages, public search, existing photo collections, video, transcripts, timestamps, browser access, and relevant installed skills/tools.
- [references/worldsfair-2026-journey.md](references/worldsfair-2026-journey.md): detailed real-world case study, concrete incorrect matches, identity corrections, production counts, failed hypotheses, and eight benchmarked padding recoveries.
- [references/implementation-and-operations.md](references/implementation-and-operations.md): existing SCRFD/SFace implementation, image-quality gates, operational commands, repository paths, regression checks, and release verification.

## Distinguish the five problems

1. **Identity coverage:** Do we even know that this person belongs in the searchable population? The source might be a customer directory, employee roster, contact list, event program, cast list, photo metadata, video title, caption, or prior human review.
2. **Person identity:** Does a profile, document, media item, or candidate name actually refer to the right individual? Similar names, employers, public aliases, organizations, and historical affiliations are evidence, not identity by themselves.