Facial Emotion Detection

A computer vision project that detects facial emotions from webcam input by extracting facial landmarks and classifying them with a machine learning model.

Why this project stands out

This project demonstrates a full end-to-end machine learning workflow:

• collecting structured facial data,
• training a classification model,
• and deploying it in a live real-time demo.

It is a strong example of applied Python, OpenCV, and scikit-learn skills for roles in AI, computer vision, and data science.

Key features

• Real-time face mesh landmark extraction
• Emotion data collection from webcam input
• Training pipeline for a supervised classifier
• Live demo for instant emotion prediction
• Clean, modular project structure for easier extension

Tech stack

• Python
• OpenCV
• cvzone
• NumPy
• pandas
• scikit-learn

Project structure

Emotion-Detection/
├── data/
│   └── data.csv
├── models/
│   └── model.pkl
├── scripts/
│   ├── common.py
│   ├── generate_data.py
│   ├── train_model.py
│   └── live_demo.py
├── datagen.py
├── training.py
├── test.py
├── requirements.txt
└── README.md

Getting started

1. Clone the repository

git clone <your-repository-url>
cd Emotion-Detection

2. Create a virtual environment

python -m venv .venv
source .venv/bin/activate   # macOS/Linux
.venv\Scripts\activate      # Windows

3. Install dependencies

pip install -r requirements.txt

How the project works

1. Data collection
   • Run the data collection script to capture facial landmarks and label them.
2. Model training
   • Train a classifier on the collected dataset.
3. Live prediction
   • Run the demo to classify emotions from a webcam stream.

Usage

Collect data

python datagen.py

Train the model

python training.py

Run the live demo

python test.py

What I learned from building this project

• Working with computer vision pipelines
• Preparing structured data for machine learning
• Building a practical ML demo from scratch
• Organizing a project for readability and maintainability

Future improvements

• Add more emotion classes
• Improve model accuracy with more diverse data
• Experiment with deep learning models
• Add a GUI or web interface

License

This project is intended for educational and portfolio purposes.