Face Expression Recognition Project

Introduction

This project is a machine learning model designed for face expression recognition. It analyzes facial expressions from images and classifies them into different categories. The dataset used for training was downloaded from Kaggle using the kagglehub library. The model has achieved an accuracy of 75.29%.

How It Works

1. Dataset Acquisition:

   • The dataset, containing labeled facial expressions, was downloaded from Kaggle using the kagglehub library.
   • Link to dataset: face expression recognition dataset

2. Preprocessing:

   • Images were resized, normalized, and augmented to enhance model performance.
   • Data cleaning was performed to remove any noisy or irrelevant samples.

3. Model Training:

   • A deep learning model was trained using frameworks like TensorFlow/Keras or PyTorch.
   • Convolutional Neural Networks (CNNs) were used for feature extraction and classification.
   • The model was evaluated on a test dataset and achieved an accuracy of 75.29%.

4. Deployment & Usage:

   • The trained model can classify new images into different facial expression categories.
   • It can be integrated into applications for real-time emotion detection.

Requirements

To run this project, ensure you have the required dependencies installed. You can install them using:

pip install -r requirements.txt

Files in the Project

• Dataset Files: Downloaded using kagglehub
• Model Training Scripts: Scripts used to train and evaluate the model
• requirements.txt: Lists all the dependencies required to run the project

Conclusion

This project demonstrates a machine learning pipeline for face expression recognition, from dataset acquisition to model evaluation. The achieved accuracy of 75.29% indicates a promising model, with potential improvements through hyperparameter tuning, data augmentation, and deeper architectures.