Murtaza-mahudawala/Emotion_Detection_Python

This repository features a deep learning model for real-time emotion detection from facial expressions using a webcam. Built with Keras and TensorFlow, it classifies emotions such as anger, disgust, fear, happiness, neutrality, sadness, and surprise. This leverages Haar Classifier for face detection and processes images for optimal performance.

Emotion Detection Using Python and TensorFlow 🎭 Overview This project is a deep learning-based solution to detect and classify human emotions from facial images. It identifies 7 emotions:

Angry Disgust Fear Happy Neutral Sad Surprise The model is trained on a dataset of approximately 30,000 images, ensuring high accuracy and robustness across diverse settings.

Features Accurate Emotion Detection: Leverages Convolutional Neural Networks (CNN) for high performance. Scalable: Easy to extend to more emotion classes or integrate with real-time applications. Customizable: Flexible codebase for adapting to other datasets or use cases. Prerequisites Ensure you have the following installed:

Python 3.7 or later TensorFlow Other libraries: numpy, pandas, matplotlib, tensorflow.keras, etc. To install dependencies, run:

Copy code pip install -r requirements.txt Dataset The model was trained using publicly available datasets with labeled images for different emotions. You can download similar datasets from platforms like Kaggle or use your own.

Getting Started

1. Clone the Repository bash Copy code git clone https://github.com/Murtaza-mahudawala/Emotion_Detection_Python.git cd your-repo-link
2. Training the Model To train the model on your dataset:

File Structure train.py: Script to train the model. test.py: Script to test the model with individual images. requirements.txt: Python dependencies. Results Accuracy: Achieved 51% accuracy on the validation set. Sample Predictions:

Applications Mental Health: Recognize signs of stress or sadness. Customer Experience: Gauge emotional responses during interactions. Education: Monitor student engagement and emotions in virtual classes. Contributing Contributions are welcome! Please follow these steps:

Fork the repository. Create a new branch (git checkout -b feature-branch). Commit your changes (git commit -m "Add new feature"). Push the branch (git push origin feature-branch). Create a Pull Request. License This project is licensed under the MIT License - see the LICENSE file for details.

Contact For any queries, feel free to reach out:

Your Name: Murtuza Mahudawala Email: mmahudawala4@gmail.com