This project is aimed at building a comprehensive face recognition system with emotion detection capabilities. The project encompasses multiple phases, including machine learning, deep neural networks, and web application development using Django.
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In this project, we have implemented the following phases:
In this phase, we perform face identification using machine learning techniques. Here's an overview of the steps involved:
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Data Preprocessing: We preprocess the images, ensuring they are suitable for training.
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Feature Extraction: Deep neural networks are used to extract features from facial images.
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Modelling: We train various machine learning models, such as logistic regression, support vector machines, and random forest, using the features extracted from celebrity images.
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Ensemble Learning: A voting classifier is employed to combine the predictions of individual models.
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Hyperparameter Tuning: We fine-tune the hyperparameters to optimize the final model.
In this phase, we focus on emotion recognition using convolutional neural networks (CNNs). The process is as follows:
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Data Preprocessing: We preprocess the images, ensuring they are suitable for training.
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Modelling: We train a convolutional neural network to recognize emotions from facial expressions.
In the final phase, we create a web application using Django. The development process includes the following steps:
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Web Frontend: We render the frontend using HTML, CSS, and Bootstrap for an appealing user interface.
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Backend: The backend is powered by Python and the Django framework, following the MVT (Models, Views, Templates) architecture.
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Database: We design and utilize an SQLite database for data storage.
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Integration: Both the face recognition and emotion detection models are integrated into the web app to provide face analysis.
To run this project locally, follow these steps:
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Clone the repository to your local machine.
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Navigate to the project directory.
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Install the necessary dependencies using the following command:
pip install -r requirements.txt -
Get SECRET_KEY:
from django.core.management.utils import get_random_secret_key print(get_random_secret_key()) -
Run the Django development server:
python manage.py runserver -
Access the web application by opening a web browser and entering the following URL:
http://localhost:8000/
Contributions to this project are welcome! If you have any improvements or suggestions, please open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
We would like to thank the open-source community for their valuable contributions and the dataset providers for making their data available for research and development.