Face Recognition using TensorFlow and Keras

This project demonstrates a simple face recognition system using TensorFlow and Keras. It captures an image from a webcam, detects faces using the MTCNN (Multi-Task Cascaded Convolutional Networks) face detector, and compares the detected faces with a pre-trained FaceNet model.

Installation

To install the required dependencies, follow these steps:

Install Python 3.7 or higher: Download Python
Create a virtual environment (optional):
- Install virtualenv using pip:
```
pip install virtualenv
```
- Create a new virtual environment:
```
virtualenv venv
```
- Activate the virtual environment:
  - On Windows:
```
venv\Scripts\activate
```
  - On macOS/Linux:
```
source venv/bin/activate
```

Clone this repository:

git clone https://github.com/ape-with-helmet/LiveVideoMap.git

Navigate to the project directory:
```
cd LiveVideoMap
```
Install the required packages:
```
pip install -r requirements.txt
```

Usage

To run the face recognition program, execute the following command:

python face_feed.py

The program will open a window displaying the live video feed from your webcam. Press 's' to capture an image of an Aadhaar card face, and press 'q' to quit the program.

The program will compare the captured Aadhaar card face with the detected faces in the live video feed and display the match results.

Notes

Make sure you have a compatible NVIDIA GPU and the appropriate CUDA drivers installed for TensorFlow GPU support.
The pre-trained FaceNet model used in this example is provided by the keras-facenet library.
The program uses the MTCNN face detector from the mtcnn library.

License

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