This project focuses on the integration of emotion detection systems into real-world applications using advanced technologies such as Convolutional Neural Networks (CNN) and OpenCV. Emotion detection plays a crucial role in various fields, including mental health, human-computer interaction, business insights, security, and sentiment analysis. By leveraging deep learning models for facial emotion recognition and feature extraction, these systems offer valuable insights into individuals' emotional states, customer sentiments, and security concerns.
The project achieves an accuracy of 60% in emotion detection, showcasing its effectiveness in real-world scenarios. It explores the application of emotion detection systems in mental health scenarios to monitor emotional well-being and support individuals effectively. Additionally, it delves into enhancing human-computer interaction experiences by enabling personalized interactions based on user emotions. In business settings, these systems provide valuable customer insights by analyzing emotions and sentiments to tailor products and services accordingly.
This project is a demonstration of an Emotion Detection System implemented in Python. It uses machine learning techniques to recognize emotions from images.
- Python (version 3.8 - 3.11)
Follow these steps to get started with the Emotion Detection System:
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Clone the repository to your local machine using the following command:
git clone https://github.com/Sparshcodies/Emotion-Detection-System
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Navigate to the project directory:
cd Emotion-Detection-System -
Install the required dependencies using pip:
pip install -r requirements.txt
Once you have installed all the dependencies, you can run the GUI for the Emotion Detection System by executing the following command:
python GUI.pyThis will launch the graphical user interface where you can interact with the system to detect emotions from images.
If you have any feedback, please reach out to us at sparshsahu567@gmail.com
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