https://github.com/e1washere/emotion-recognition-app

Emotion Recognition App is a cross-platform desktop app that uses deep learning to detect emotions from facial images and videos. It runs fully offline and supports real-time analysis using 8 models built with TensorFlow and PyTorch. Developed with React, Electron, and FastAPI.
https://github.com/e1washere/emotion-recognition-app

deep-learning emotion-recognition fastapi pytorch react tensorflow

Last synced: 3 months ago
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Emotion Recognition App is a cross-platform desktop app that uses deep learning to detect emotions from facial images and videos. It runs fully offline and supports real-time analysis using 8 models built with TensorFlow and PyTorch. Developed with React, Electron, and FastAPI.

• Host: GitHub
• URL: https://github.com/e1washere/emotion-recognition-app
• Owner: e1washere
• License: mit
• Created: 2025-05-07T11:27:00.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-07-23T20:03:02.000Z (11 months ago)
• Last Synced: 2026-01-03T16:12:11.098Z (6 months ago)
• Topics: deep-learning, emotion-recognition, fastapi, pytorch, react, tensorflow
• Language: Python
• Homepage:
• Size: 1.19 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Emotion Recognition App 😄😢😠

[![License: MIT](https://img.shields.io/badge/License-MIT-brightgreen.svg)](https://opensource.org/licenses/MIT)

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[![Documentation](https://img.shields.io/badge/Docs-Stable-blue.svg)](https://github.com/e1washere/emotion-recognition-app/wiki)

![Python Version](https://img.shields.io/badge/python-3.9-blue.svg)

[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/e1washere/emotion-recognition-app/badge)](https://securityscorecards.dev/viewer/?uri=github.com/e1washere/emotion-recognition-app)

A cross-platform desktop app that uses deep learning to recognize emotions from facial images or video using 8 models (TensorFlow + PyTorch). Developed with React + Electron + FastAPI.

## 📸 Screenshots

### Video Upload Recognition

![Video Recognition](screenshots/video_recognition.png)

## 👨‍💻 Tech Stack

- Frontend: ReactJS, Electron, FaceAPI.js

- Backend: Python, FastAPI

- ML: TensorFlow, PyTorch (VGG16 models)

## 💡 Features

- Real-time webcam-based emotion recognition

- Upload image/video for analysis

- Compare 8 emotion models

- Fully local app (no cloud required)

## 🧠 Datasets Used

- FER2013

- KDEF

- NHFI

- Mixed Dataset

## 🧪 Model Training

Each dataset was used to train two models (TensorFlow + PyTorch). A two-phase transfer learning strategy was used with VGG16 as the base model.

## 📊 Model Results

| Dataset  | Framework  | Accuracy | F1 Score |

|----------|------------|----------|----------|

| KDEF     | TensorFlow | 92%      | 0.92     |

| KDEF     | PyTorch    | 91%      | 0.91     |

| NHFI     | TensorFlow | 78%      | 0.76     |

| NHFI     | PyTorch    | 72%      | 0.71     |

| FER2013  | TensorFlow | 67%      | 0.65     |

| FER2013  | PyTorch    | 66%      | 0.63     |

| Mixed    | TensorFlow | 70%      | 0.70     |

| Mixed    | PyTorch    | 68%      | 0.68     |

## 🚀 How to Run It

### Backend

```bash

cd Backend

pip install -r requirements.txt

uvicorn app.main:app --reload