https://github.com/ammahmoudi/face-recognition

Face recognition using Eigen faces, PCA and support vector machines
https://github.com/ammahmoudi/face-recognition

eigenfaces face-recognition machine-learning ml pca svm

Last synced: 5 days ago
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Face recognition using Eigen faces, PCA and support vector machines

• Host: GitHub
• URL: https://github.com/ammahmoudi/face-recognition
• Owner: ammahmoudi
• Created: 2023-10-29T21:23:54.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-10-29T21:34:30.000Z (over 2 years ago)
• Last Synced: 2025-12-05T01:43:46.759Z (6 months ago)
• Topics: eigenfaces, face-recognition, machine-learning, ml, pca, svm
• Language: Jupyter Notebook
• Homepage:
• Size: 2.2 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Face Classification by SVM on Eigenfaces

We are going to build a classifier (Face recognition using Eigen faces, PCA and support vector machines) to distinguish the faces of 40 people on a toy dataset. The dataset includes 400 pictures of 40 people faces, each by a 64*64 pixel picture.

![samples of data](/samples.png)

## Code Explanation

### **Dataset and Visualization**:

- The dataset consists of **400 pictures** of **40 people's faces**, each represented by a **64x64 pixel image**.

- The code visualizes the first image of each class (person) using subplots.

### **Train-Test Split**:

- The data is split into train and test sets with a **70% train** and **30% test** split.

- Dimensions of the sets are printed.

### **Dimensionality Reduction (PCA)**:

- Principal Component Analysis (PCA) is used to reduce dimensionality.

- The number of components is chosen to keep **90% variance**.

- Scree plot shows the proportion of variance explained by each component.

### **Eigenfaces Visualization**:

- Eigenfaces are the top eigenvectors from PCA.

- The first 30 eigenfaces are plotted, showing facial features.

    ![samples of data](/eigen.png)

### **Transforming Data with PCA**:

- Train and test features are transformed using PCA.

### **SVM Classifier Training**:

- An SVM classifier is trained on the transformed data.

- Grid search is used for hyperparameter tuning.

### **Model Evaluation**:

- Model predictions are checked on test samples.

- Precision-Recall tradeoff is visualized.

- Decision threshold where recall equals precision is determined.

### **ROC/AUC Comparison**:

- A Random Forest classifier with 30 estimators is trained.

- ROC curve and AUC are calculated for both SVM and Random Forest.

- SVM has an AUC of 0.98, indicating good performance.

### **Classification Report and Confusion Matrix**:

- Classification report shows precision, recall, and F1-score for each class.

- Confusion matrix visualizes true/false classifications per class.