Mini-Project for Fundamentals of Machine Learning Course

This repository contains the code and data for a mini-project on facial expression recognition using machine learning algorithms.

📑 Project Policy

Team: group should consist of 3-4 students.

No. Student Name Student ID

1 Nguyễn Trung Đức 21110269

2 Nguyễn Ngọc Huynh 21110310

3 Lê Công Khánh 21110320

4
The submission deadline is strict: 11:59 PM on June 22nd, 2024. Commits pushed after this deadline will not be considered.

No.	Student Name	Student ID
1	Nguyễn Trung Đức	21110269
2	Nguyễn Ngọc Huynh	21110310
3	Lê Công Khánh	21110320
4

📄 Report

The dataset is imbalanced: The least common label is 1 (Disgust) and the most common is 3 (Happy)
This imbalance led to the precision of class 3 in the confusion matrix having the highest score, while class 1 has the lowest score. This indicates that models is more biased towards class 3

Confusion matrix of Random Forest model on the original dataset

The dimension of the images is large: (1 x 48 x 48 = 2304 pixels (features)). Therefore, dimension reduction is necessary to achieve faster training times. Models run on dimension-reduced dataset by PCA train faster than those using the original data

Because training took longer on Google Colab, we benchmarked on a machine with a 12 core, 24 thread, 3.9Hz CPU, for 3 models (KNN, Logistic Regression, Random Forest):

	Original Data	PCA Data
Training	$\approx$ 10 minutes	$\approx$ 2 minutes
Parameters Tuning	$\approx$ 1 hour 10 minutes	$\approx$ 30 minutes
Cross Validation	$\approx$ 30 minutes	$\approx$ 15 minutes

We also experimented with SVM for the dataset. Although it is slower than other ML algorithms, it gave slightly better results
Besides that, we experimented with 2 CNN models: VGG and ResNet. We could train them to outperform 3 ML models we used, but due to time and resource limitations, we could not train for more epochs. So we just used the last layer in both the VGG and ResNet models to train for 2 epochs.

💪 Performance

Performance of models on test set:

Model	Accuracy	Precision	Recall	F1
KNN	0.34	0.36	0.34	0.33
Logistic Regression	0.35	0.33	0.35	0.34
Random Forest	0.47	0.49	0.47	0.45
VGG (2 epochs)	0.25	-	-	-
ResNet (2 epochs)	0.33	-	-	-

=> The Random Forest model gave the best performance on different metrics among the others.

Other improvements:

Oversampling and Undersampling to prevent imbalance
Experimenting with other simple Deep Learning models and comparing them with more Machine Learning models

📦 Project Structure

The repository is organized into the following directories:

/data: This directory contains the facial expression dataset. You'll need to download the dataset and place it here before running the notebooks. (Download link provided below)
/notebooks: This directory contains the Jupyter notebook EDA.ipynb. This notebook guides you through exploratory data analysis (EDA) and classification tasks.

⚙️ Usage

This project is designed to be completed in the following steps:

Fork the Project: Click on the Fork button on the top right corner of this repository, this will create a copy of the repository in your own GitHub account. Complete the table at the top by entering your team member names.
Download the Dataset: Download the facial expression dataset from the following link and place it in the /data directory:
Complete the Tasks: Open the notebooks/EDA.ipynb notebook in your Jupyter Notebook environment. The notebook is designed to guide you through various tasks, including:
1. Prerequisite
2. Principle Component Analysis
3. Image Classification
4. Evaluating Classification Performance
Make sure to run all the code cells in the EDA.ipynb notebook and ensure they produce output before committing and pushing your changes.
Commit and Push Your Changes: Once you've completed the tasks outlined in the notebook, commit your changes to your local repository and push them to your forked repository on GitHub.