OmarEhab007 / Facial__Keypoint_Detection

udacity computer vision Nano-degree frist project. In this project, I’ll combine your knowledge of computer vision techniques and deep learning architectures to build a facial keypoint detection system. Facial keypoints include points around the eyes, nose, and mouth on a face and are used in many applications. These applications include: facial tracking, facial pose recognition, facial filters, and emotion recognition

Facial Keypoint Detection

This repository contains a project that performs facial keypoint detection using Convolutional Neural Networks (CNNs). The model was trained on a dataset of facial images and their corresponding facial keypoints using PyTorch.

Getting Started

To get started with this project, follow the instructions below:

Prerequisites

To run this project, you need to have the following software installed:

• Python 3.x
• PyTorch
• OpenCV
• Matplotlib
• NumPy
• Jupyter Notebook

Installation

To install the required software, follow the instructions below:

1. Install Python 3.x from the official Python website.

2. Install PyTorch by following the instructions on the official PyTorch website.

3. Install OpenCV by running the following command:

pip install opencv-python

4. Install Matplotlib by running the following command:

pip install matplotlib

5. Install NumPy by running the following command:

pip install numpy

6. Install Jupyter Notebook by running the following command:

pip install jupyter

Usage

To use this project, follow the instructions below:

1. Clone this repository by running the following command:

git clone https://github.com/OmarEhab007/Facial__Keypoint_Detection.git

2. Navigate to the cloned repository by running the following command:

cd Facial__Keypoint_Detection

3. Launch Jupyter Notebook by running the following command:

jupyter notebook

4. Open the Facial_Keypoint_Detection.ipynb notebook in Jupyter Notebook.

5. Follow the instructions in the notebook to train the model and perform facial keypoint detection on an image.

Dataset

The dataset used in this project is the Facial Keypoints Detection dataset, which contains 7049 training images and 1783 test images. The dataset includes images with faces marked with 68 facial keypoints, such as the corners of the mouth and the eyes.

Acknowledgments

• This project was inspired by the Udacity Computer Vision Nanodegree Program.
• The dataset used in this project was obtained from the Kaggle Facial Keypoints Detection competition.