Facial Emotion Recognition Preprocessing

This project preprocesses facial emotion images for machine learning tasks. It includes image resizing, normalization, and data augmentation to improve model training.

Dataset

The dataset can be downloaded from: Facial Emotion Dataset

After downloading, place the dataset in the project directory at:

D:\Mini ML\archive (3)

Features

• Image resizing to 224x224 pixels
• RGB color space conversion
• Pixel normalization (0-1 range)
• Data augmentation using Albumentations:
  • Horizontal flipping (30% probability)
  • Random rotation (±15 degrees)
  • Random brightness and contrast adjustments

Requirements

The project requires Python 3.13+ and the following packages:

numpy>=2.0.0
opencv-python>=4.8.0
albumentations>=1.3.1
scikit-learn>=1.4.0
tqdm>=4.66.1

To install the requirements:

pip install -r requirements.txt

Project Structure

Mini ML/
├── archive (3)/          # Original dataset directory
│   ├── train/           # Training images
│   └── test/            # Testing images
├── processed_data/       # Output directory for preprocessed images
├── preprocessing.py      # Main preprocessing script
├── requirements.txt      # Package dependencies
└── README.md            # This file

Usage

1. Download the dataset from the provided Google Drive link
2. Install the requirements using pip
3. Run the preprocessing script:

python preprocessing.py

The preprocessed images will be saved in the processed_data directory, maintaining the same folder structure as the original dataset.