Face And Emotion Detection

Performing image classification for detection of various human emotions using CNN Architecture.

AI & Machine LearningJupyter NotebookMIT

Abstract

Face And Emotion Detection is an open-source AI & Machine Learning project. Performing image classification for detection of various human emotions using CNN Architecture. The dataset taken was "fer2013". It can be downloaded through the link "https://github.com/npinto/fer2013". It is built using Jupyter Notebook, OpenCV, TensorFlow, Keras. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

The dataset taken was "fer2013". It can be downloaded through the link "https://github.com/npinto/fer2013". Image augmentation was performed on this data.

The model architecture consists of CNN Layer, Max Pooling, Flatten and Dropout Layers.

The model was trained by using variants of above layers mentioned in model building and by varying hyperparameters. The best model was able to achieve 60.1% of validation accuracy.

2. Objective

Performing image classification for detection of various human emotions using CNN Architecture.

This project demonstrates how Jupyter Notebook, OpenCV, TensorFlow can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

Jupyter NotebookOpenCVTensorFlowKeras

5. System Requirements

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

  • Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
  • pip for dependencies
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/soumyajit4419/Face_And_Emotion_Detection.git
cd Face_And_Emotion_Detection

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 soumyajit4419 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.

View Source on GitHubDownload ZIP

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