Face Recognition System

This is a Face Recognition System built using Python and various machine learning algorithms. The system detects and recognizes faces in real-time or from images, utilizing OpenCV for face detection and machine learning techniques for face recognition.

Table of Contents

• Introduction
• Features
• Technologies Used
• Setup Instructions
• Usage
• Project Structure
• Future Enhancements
• Contributing
• License

Introduction

This Face Recognition System identifies human faces using machine learning algorithms. It detects faces in real-time from a webcam feed or from an image file and compares the detected face with a pre-existing dataset to identify the person.

Features

• Real-time face detection using OpenCV.
• Face recognition through machine learning algorithms.
• Supports image-based face recognition.
• Easy integration and extensibility with different datasets.

Technologies Used

• Python 3.x: The programming language used for the project.
• OpenCV: Library for image processing and face detection.
• NumPy: Used for numerical operations.
• Machine Learning Algorithms: Includes techniques like KNN, SVM, and deep learning for face recognition.
• Haar Cascade Classifier: For face detection.
• CSV: Used to store the attendance list in CSV format.

Setup Instructions

Prerequisites

• Python 3.x
• Git (for cloning the repository)
• Required Python libraries: Install via requirements.txt.

Installation

1. Clone the repository:

   git clone https://github.com/Samima-Nasrin/Face-Recognition-System.git