Emotion Detection

Abstract

1. Introduction

This project aims to classify the emotion on a person's face into one of seven categories, using deep convolutional neural networks. The model is trained on the FER-2013 dataset which was published on International Conference on Machine Learning (ICML). This dataset consists of 35887 grayscale, 48x48 sized face images with seven emotions - angry, disgusted, fearful, happy, neutral, sad and surprised.

2. Objective

Real-time Facial Emotion Detection using deep learning

This project demonstrates how Python, Deep Learning, Computer Vision can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

• Python 3, OpenCV, Tensorflow
• To install the required packages, run pip install -r requirements.txt.

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/atulapra/Emotion-detection.git
cd Emotion-detection

1. First, clone the repository and enter the folder
2. Download the FER-2013 dataset inside the src folder.
3. If you want to train this model, use:
4. If you want to view the predictions without training again, you can download the pre-trained model from here and then run:
5. The folder structure is of the form:
6. data (folder)
7. emotions.py (file)
8. haarcascade_frontalface_default.xml (file)

git clone https://github.com/atulapra/Emotion-detection.git
cd Emotion-detection

cd src
python emotions.py --mode train

cd src
python emotions.py --mode display

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by atulapra and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.