Face-Detection

Face Detection using Haarcascade and YOLOv8

This repository contains code for performing face detection on a video using both Haarcascade and YOLOv8 algorithms. The project aims to demonstrate the effectiveness of these two approaches in detecting faces in a video.

Introduction

Face detection is a crucial task in computer vision with various applications ranging from security surveillance to facial recognition systems. In this project, we utilize two popular face detection algorithms:

1. Haarcascade: A machine learning-based approach that detects objects in images or video based on a set of feature descriptors.
2. YOLOv8: You Only Look Once (YOLO) is a state-of-the-art, real-time object detection system that achieves high accuracy with fast detection speed.

The aim of this project is to compare the performance of these two algorithms in detecting faces in a video.

Demo Output Video

https://github.com/SannketNikam/Face-Detection/assets/77570082/9ae32a48-e880-4722-affc-e6f050b65494

Getting Started

1. Clone the repository:

git clone https://github.com/SannketNikam/Face-Detection.git

1. Navigate to the project directory:

cd Face-Detection

1. Install the required dependencies:

pip install -r requirements.txt