Face Detection

Welcome to the Face Detection GitHub repository! This repository contains the code and resources for the Face Detection project, which is a task assigned by CodeClause Internship. The goal of this project is to develop a system that can detect and localize human faces in images or video streams.

Table of Contents

• Introduction
• Installation
• Usage
• Data
• Model
• Results

Introduction

Face Detection is a computer vision project that focuses on developing an algorithm capable of detecting and localizing human faces in images or video streams. The system utilizes state-of-the-art deep learning techniques to accurately identify the presence and location of faces, enabling various applications such as face recognition, emotion analysis, and more.

Installation

To run the Face Detection project locally, please follow the instructions below:

1. Clone this repository to your local machine using the following command:

   git clone https://github.com/gandharvk422/Face-Detection.git
   

2. Navigate to the project directory:

   cd Face Detection
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

To use the Face Detection system, follow these steps:

1. Ensure that you have installed all the dependencies as mentioned in the installation section.

2. Prepare the image or video data you want to perform face detection on.

3. Run the detection script:

   python Face Detection.py
   

4. The system will process the input data and display the results, marking the detected faces with bounding boxes and labels if applicable.

Data

The Face Detection project relies on pre-trained cascade classifiers for detecting faces and eyes. The following pre-trained classifiers are used:

1. haarcascade_frontalface_alt.xml: This cascade classifier is specifically trained to detect frontal faces in images. It is widely used for face detection tasks and provides good accuracy.

2. haarcascade_eye_tree_eyeglasses.xml: This cascade classifier is trained to detect eyes, including the presence of eyeglasses. It can be used in conjunction with the face classifier to detect and localize eyes within detected faces.

You can find these pre-trained cascade classifiers in the data directory of this repository. The classifiers are provided in XML format and can be directly used with OpenCV or other libraries/frameworks that support Haar cascades for object detection.

To use these cascade classifiers in your Face Detection system, you can load them using the appropriate libraries and apply them to your input images or video frames. The classifiers will provide bounding box coordinates for the detected faces and eyes, allowing you to visualize or further process the results.

Please note that these pre-trained classifiers are just one approach to face and eye detection. If you have access to labeled datasets, you can also train your own custom models using deep learning techniques for improved accuracy and performance.

Feel free to explore the data directory to access the pre-trained cascade classifiers and use them in your Face Detection project.

Model

The Face Detection system utilizes deep learning models that are specifically designed for object detection tasks. State-of-the-art models like Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO), or Faster R-CNN can be used for face detection.

The repository provides code for training your own face detection model or using pre-trained models available in popular deep learning frameworks like TensorFlow or PyTorch.

Results

The performance and results of the Face Detection system can be found in the repository's documentation. I have included detailed analysis and evaluation metrics to measure the accuracy, precision, and recall of the model on various datasets.