Emotion Tracker

Abstract

1. Introduction

A professional Python desktop application for real-time facial emotion detection using DeepFace. The application analyzes user emotions via webcam and stores data as time-series CSV files with comprehensive analytics.

2. Objective

AI-powered real-time emotion detection desktop app using DeepFace. Track emotions via webcam with privacy-focused local processing, analytics dashboard, and CSV export.

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

3. Key Features / Modules

• Real-time Emotion Detection: Uses DeepFace for accurate emotion recognition
• Interactive GUI: Three-tab interface (Control, Monitor, Analytics)
• Performance Optimized: Processes every 3rd frame to reduce CPU load
• Data Storage: Automatically saves daily CSV files with timestamps
• Visual Analytics: Pie charts and statistics for emotion analysis
• Privacy-Focused: All data stored locally, no internet transmission

4. Technology Stack

• CPU Usage: <15% on modern i5 processors
• RAM Usage: <500MB
• Detection Latency: <2 seconds per processed frame
• UI Responsiveness: No freezing during processing
• deepface==0.0.79
• opencv-python==4.8.1.78
• pandas>=2.0.0,<2.2.0
• matplotlib>=3.7.0,<3.9.0

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/DeepPythonist/EmotionTracker.git
cd EmotionTracker

1. Clone this repository:
2. Create virtual environment:
3. Install dependencies:
4. Run the application:

git clone https://github.com/DeepPythonist/EmotionTracker.git
   cd EmotionTracker

conda create -n emotiontracker python=3.10
   conda activate emotiontracker

pip install -r requirements.txt

python main.py

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 DeepPythonist 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.