Facial Recognition using Generative Adversarial Networks (GAN)

This project implements a facial recognition model using Generative Adversarial Networks (GAN). The model is capable of generating realistic facial images and recognizing faces from input images.

Overview

Generative Adversarial Networks (GANs) are a type of deep learning model consisting of two neural networks, the generator and the discriminator, which are trained together in a competitive manner. In this project, we utilize a GAN architecture to generate facial images and then recognize faces from input images.

Requirements

• Python 3.x
• TensorFlow 2.x
• Keras
• OpenCV
• NumPy
• Matplotlib

Installation

1. Clone the repository:

git clone https://github.com/shahpalash10/facial-recognition-gan.git
cd facial-recognition-gan

2. Install the dependencies:

pip install -r requirements.txt

Usage

1. Run the following command to train the GAN model:

python train.py

2. After training, you can use the trained model for facial recognition:

python recognize_faces.py --input <path_to_input_image>

Replace <path_to_input_image> with the path to the input image you want to perform facial recognition on.

Dataset

The dataset used for training the GAN model consists of facial images. You can use any facial image dataset of your choice, or even a custom dataset.

Model Architecture

The GAN model architecture consists of a generator and a discriminator network. The generator generates realistic facial images from random noise, while the discriminator distinguishes between real facial images and fake (generated) facial images.

Results

The trained GAN model can generate realistic facial images and perform facial recognition accurately on input images.

Contributing

Contributions are welcome! Please feel free to fork this repository and submit pull requests to contribute to this project.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• This project was inspired by the works on GANs and facial recognition in the field of deep learning.
• Special thanks to the contributors of the dependencies used in this project.

Contact

For any inquiries or suggestions, please contact [email protected].

Thank you for using our facial recognition model! We hope it serves your needs effectively.

You can copy and paste this README directly into your GitHub repository.