Face Verification and Recognition using Siamese Neural Network

This is a face recognition project that utilizes a Siamese Neural Network for accurate and efficient face recognition. The project is implemented in a Jupyter Notebook, making it easy to run and experiment with.

Table of Contents

• Introduction
• Setup
• Usage
• Dataset
• Model Architecture
• Results
• Contributing

Introduction

Face Verification and Recognition is a crucial computer vision task that involves locating and identifying human faces within images or video frames. The Siamese Neural Network is a powerful architecture that learns to distinguish between pairs of images and can be applied effectively for face detection.

This project focuses on utilizing a Siamese Neural Network to perform real-time face detection. The model is designed to identify whether two given images contain the same person's face or not, which is a fundamental concept in face recognition and verification tasks.

Setup

To run this project, you'll need the following environment:

1. Python >= 3.8
2. Jupyter Notebook
3. Required libraries: TensorFlow, Keras, OpenCV, NumPy.

You can install the necessary libraries using pip:

pip install tensorflow keras opencv-python numpy

Usage

1. Clone this repository to your local machine.
2. Open the Jupyter Notebook face_Detection.ipynb.
3. Follow the instructions provided in the notebook to execute each code cell.

The Jupyter Notebook contains detailed explanations for each step of the face detection process, making it easy to understand the workflow.

Dataset

This project requires a dataset of images containing faces for training the Siamese Neural Network. Ideally, the dataset should consist of pairs of images, with each pair containing two images of the same person's face and two images of different people's faces.

Due to the size and licensing restrictions of face datasets, we cannot include the dataset in this repository. However, you can use publicly available face datasets or collect and prepare your own dataset for this specific task.

Model Architecture

The Siamese Neural Network architecture used in this project consists of two identical subnetworks (twins) that share the same weights. Each twin processes one image from the pair, and the network learns to compare the representations of both images to determine if they belong to the same person or not.

The Jupyter Notebook also compares the face embedding of the input image to the saved embeddings for face recognition.

Results

The results of the face detection process will be displayed in the Jupyter Notebook. The model will take pairs of test images as input and output a similarity score indicating whether the images contain the same face or not.

Contributing

Contributions to this project are welcome. If you find any issues or want to add improvements, please create a pull request, and we'll be happy to review it.

Thank you