"Align face identity folders with MTCNN detection, landmark
Scanned 9/8/2026
Install to Claude Code
npx -y skills add VectorSpaceLab/AREX-Skill --skill face-alignment --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Face Alignment?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/vectorspacelab-face-alignment-arex-skill)More formats (shields.io, HTML) on the badges page.
---
name: face-alignment
description: "Align face identity folders with MTCNN detection, landmark
localization, affine crops, and resize-before-align helpers."
disable-model-invocation: true
metadata:
disco-role: operating
license: MIT
---
# face-alignment
Use this sub-skill when the request is about face.evoLVe's MTCNN-based face detection, landmark localization, affine alignment, crop-size scaling, or resize-before-align preprocessing.
Do not use this sub-skill for low-shot balancing or ImageFolder validation; route those to `data-preparation`. Do not use it for checkpoint feature extraction or verification; route those to `feature-extraction-verification`. PaddlePaddle duplicate alignment parity belongs with `paddle-workflows`.
## Read or run
- Read `references/alignment-workflows.md` when you need the expected identity-folder input/output layout, detector parameters, crop-size scaling, or validation checklist.
- Read `references/troubleshooting.md` when imports fail, landmarks are missing, multiple faces appear, corrupt images are skipped, colors look wrong, crop sizes are surprising, non-JPEG inputs are converted, or SyntaxWarnings appear.
- Run `scripts/align_faces.py` to batch-align a `source_root/<identity>/<image>` tree into a separate aligned destination tree.
- Run `scripts/resize_faces.py` before alignment when raw face images are very large and MTCNN detection is too slow.
## Operating contract
- `scripts/align_faces.py` requires `--repo-root` pointing to a local face.evoLVe checkout that contains the MTCNN helper code and `applications/align/{pnet,rnet,onet}.npy` weights; the weights are not bundled here and no downloads are attempted.
- The bundled scripts are deterministic, traverse identity folders in sorted order, never delete source inputs, reject overlapping source/destination roots, ignore hidden `.DS_Store`-style files, and normalize aligned or resized outputs to `.jpg`.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!