Face Recognition

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.

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.

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.