Individual skill
facial-recognition-explorer
Explains modern face recognition, NIST-class closeness, embeddings, ArcFace/AdaFace/TransFace, and qualitative lookalike comparison. Triggered by facial recognition, face embeddings, ArcFace, AdaFace, NIST FRTE, how close is FR, lookalike match, or improve this face skill.
.grok/skills/facial-recognition-explorer/SKILL.md
Source commit bf31cc83a43ed8069d0d5070c68df4250273eb29
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
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- Skill scan complete
- Yes
- Observed behavior
- Instructions only
- Last checked
- Nov 11, 2026
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Repository context
- Repository stars (not skill ratings)
- 14
- Repository last updated
- Oct 21, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- ai-agents Repository topic
- grok Repository topic
- prompt-engineering Repository topic
- workflow-automation Repository topic
- engineering Derived category
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CHANGELOG.mddoc · 567 bytesSKILL.mdcore · 4,266 bytesreferences/sota-2026.mddoc · 1,513 bytes
Read excerpt
# Facial Recognition Explorer
Default to the 2025–2026 embedding stack. Do not open with Eigenfaces unless the user asks for history.
## Current stack
1. Detect — RetinaFace, SCRFD, YuNet, BlazeFace. Transformer detectors (SFE-DETR class) for tiny faces.
2. Align — 5-point landmarks, affine warp to 112x112.
3. Embed — L2-normalized 512-D vector (IR-SE, ResNet, MobileFace, ViT / TransFace).
4. Score — cosine on unit vectors. Thresholds are model-specific. Never reuse a FaceNet 0.40 cutoff on ArcFace.
Loss lineage
- FaceNet triplet loss — useful intuition, painful mining, legacy.