Emotion Detection Using Deep Learning and OpenCV

Overview

This project is an Emotion Detection system that leverages deep learning models and OpenCV to analyze facial expressions and identify emotions. It is designed to recognize and classify emotions from facial images or video streams, providing valuable insights for various applications such as user experience enhancement, mental health monitoring, and more.

Features

• Real-time emotion detection from video feeds or images.
• Classification of emotions into categories like Happy, Sad, Angry, Surprise, Disgust, and Fear.
• Integration with OpenCV for image processing and display.

Technologies Used

• Deep Learning Framework: TensorFlow/Keras
• Image Processing: OpenCV
• Programming Language: Python

Installation

1. Clone the Repository

   git clone https://github.com/Nipunkhattri/Emotion_detection_deep_learning.git
   

2. Navigate to the Project Directory

   cd Emotion_detection_deep_learning
   

3. Create a Virtual Environment (Optional but Recommended)

   python -m venv venv
   source venv/bin/activate 
   

4. Install Required Packages

   pip install -r requirements.txt
   

Emotion Detection from Video In Real Time

1. Run the Script

   python face_detection.py
   

Feel free to reach out if you have any questions or suggestions. Happy coding! 😊