Facial Recognition

Facial recognition is a way of identifying or confirming an individual’s identity using their face. Facial recognition systems can be used to identify people in photos, videos, or in real-time.

Stacks:

Linux/Windows

Python

OpenCV

Neural Networks

Dataset:

The dataset consists of a large number of images of each person individually to train the model.

Hardware (Resources) (Storage & Compute Power & Time)

For training, we need 64 GB ram with the latest processors and GPU. For just inference, we can run it on any minimum requirements with GPU.

Workflow (Processing)

1. Collect the data set with images.

2. Train the model.

3. Test the model and its accuracy.

4. Integrate with the API or any targeted application.

End To End (Development & Integration in a System)

Following steps are included in the deployment of a Facial Recognition:

API Development

Environment Setup

Engine Installation and Configuration

Model Deployment

Server and Route Setup

Testing

Deployment (Server / API)

Linux/Windows with Python Flask API.

Applications (General Real World Use)

Access Control. Automobile Security. Immigration.

Use Case (Our Specific)

We use facial recognition to calculate the screen appearance of any political or celebrity target person.