A high-performance, browser-based AI tool designed to detect and categorize human emotions in real-time. Built using Python, DeepFace, and Streamlit, this project features a stabilized HUD and dynamic color-coded feedback.
🚀 Live Demo: Face Expression Analyzer
- Real-Time Detection: Instantly identifies emotions like Happy, Neutral, Sad, Surprise, and Angry.
- Stabilized Tracking: Uses Temporal Smoothing (Linear Interpolation) to prevent the tracking box from jittering during movement.
- Cloud-Optimized: Powered by WebRTC for low-latency video streaming directly in the browser.
- Professional UI: A custom-styled dark theme designed for a modern user experience.
- Dynamic HUD: The interface reacts to your emotions, changing colors instantly to match your "vibe."
- Language: Python 3.13
- AI Library: DeepFace (OpenCV backend)
- Web Framework: Streamlit
- Video Processing: Streamlit-WebRTC & PyAV
- Environment: Cloud-deployed on Streamlit Community Cloud
If you want to run this project on your D: drive:
- Clone the Repo:
git clone [https://github.com/rishav1328/Face-Expression-Analyzer.git](https://github.com/rishav1328/Face-Expression-Analyzer.git)
- Install Requirements:
pip install -r requirements.txt
- Run the App:
streamlit run app.py
Rishav Biswas
- Position: Junior HR Executive at InAmigos Foundation
- Interests: Web Development, Python, and AI-driven automation.
Distributed under the MIT License. See LICENSE for more information.