face-recognition

EE5907/EE5027 Pattern Recognition Programming Assignment CA2

SS47816

Pre-requisites

The packages I used here are very simple:

• python >= 3.6
• scipy
• numpy
• sklearn
• opencv
• troch
• torchvision
• matplotlib
• pathlib

Installation

I am using conda environment so you can directly install the environment from file:

conda create --name pr --file requirements.txt

or

pip install -r requirements.txt

Usage

All the training data are stored in data/

CNN model .pth files are stored in model/

Resulted Figures are saved in pics/

TensorBoard logs are saved in runs/

Source codes are all stored in src/

• cnn.py: run this file to train and test the CNN models
• conventional.py: run this file to test all the conventional methods
• create_dataset.py: helper functions to split the train/test datasets (you probably won't need this)

To run the code:

# Aactivate the conda environment you just created
conda activate pr

# Run the first 4 algorithms
python3 src/conventional.py

# Run the CNN model (training and testing)
python3 src/cnn.py

Results

Q1. PCA for Feature Extraction, Visualization and Classification

Q2. LDA for Feature Extraction and Classification

Q3. GMM for Clustering

Q4. SVN for Classification

Q5. Neural Networks for Classification