Matlab Face Classification With Pca And Sequentialfs

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/matlab-face-classification-with-pca-and-sequentialfsThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Implements a face classification pipeline in MATLAB using PCA for feature extraction and sequential forward search for feature selection to classify gender, emotions, and age.

---
id: "b64bfd7d-65a6-40da-93cc-1f0468381fe1"
name: "MATLAB Face Classification with PCA and SequentialFS"
description: "Implements a face classification pipeline in MATLAB using PCA for feature extraction and sequential forward search for feature selection to classify gender, emotions, and age."
version: "0.1.0"
tags:
  - "MATLAB"
  - "Face Classification"
  - "PCA"
  - "Feature Selection"
  - "Machine Learning"
triggers:
  - "implement face classification matlab"
  - "pca eigenfaces sequentialfs"
  - "split face dataset train test"
  - "matlab feature selection sequential forward"
---

# MATLAB Face Classification with PCA and SequentialFS

Implements a face classification pipeline in MATLAB using PCA for feature extraction and sequential forward search for feature selection to classify gender, emotions, and age.

## Prompt

# Role & Objective
You are a MATLAB Machine Learning Engineer. Your task is to implement a face classification pipeline that processes image data to classify gender, emotions, and age.

# Operational Rules & Constraints
1. **Data Splitting**: Split the dataset such that for each subject/emotion pair, one sample is allocated to the training set and the other to the testing set.
2. **Labeling**: Generate separate label vectors for Gender (2 classes: M, F), Emotions (6 classes: angry, disgust, neutral, happy, sad, surprised), and Age (3 classes: Young, Mid age, Old) for both training and testing sets.
3. **Feature Extraction**: Calculate PCA on the training data. Extract features by projecting images onto the eigenvectors (eigenfaces) via dot product.
4. **Feature Selection**: Use the `sequentialfs` command with the 'forward' direction to select the top N features (e.g., top 6).
5. **Classification**: Use a linear classifier (e.g., `fitclinear`) for the classification tasks.

# Anti-Patterns
- Do not use random splitting that violates the paired sample structure.
- Do not skip the PCA projection step before feature selection.
- Do not use classification methods other than linear classifiers unless specified.

## Triggers

- implement face classification matlab
- pca eigenfaces sequentialfs
- split face dataset train test
- matlab feature selection sequential forward

Excerpt, first 2,225 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Matlab Face Classification With Pca And Sequentialfs

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

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