Face Recognition Web App with Machine Learning Django Heroku

What you'll learn
Deploy Face Recognition Django Web App in Heroku Cloud
Train your own Machine Learning based Face Recognition Model in Python
Train own Facial Emotion Recognition using Machine Learning in Python
Develop Django Web App using MVT Framework
Design SQLlite Database in Django
Train Support Vector Machines, Random Forest Model for Face Recognition in Python
Debuging error while Deploying in Heroku
Interphase Machine Learning Models with MVT Framework
Build Ensemble (stacking) Machine Learning Model combining SVM and Random Forest Models in Python
Face Detection with Deep Neural Networks
OpenCV Essentials for Face Recognition
Managing Heroku Cloud
Styling Django Web App with Bootstrap

Description
Welcome to the Course Deploy Face Recognition Web App, Machine Learning, Django & Database in Heroku Cloud which is an Artificial Intelligence Project.

Face recognition is one of the most widely used in my application. If at all you want to develop and deploy the application on the web only knowledge of machine learning or deep learning is not enough. You also need to know the creation of pipeline architecture and call it from the client-side, HTTP request, and many more. While doing so you might face many challenges while developing the app. This course is structured in such a way that you can able to develop the face recognition-based web app from scratch.

What you will learn?

Prerequisite of Project: OpenCV

Image Processing with OpenCV

Face Detection with Viola-Jones and Deep Neural Networks (SSD)

Feature Extraction with OpenCV and Deep Learning Networks

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Project Phase - 1: Face Recognition and Person Identity

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Gather Images

Extract Faces only from Images

Labeling (Target output) Images

Data Preprocessing

Training Face Recognition with OWN Machine Learning Models.

Logistic Regression

Support Vector Machines

Random Forest Classifier

Combine All Machine Learning Models with Voting Classifier

Tuning Machine Learning Model

Model Evaluation

Precision

Recall

Sensitivity

Specificity

F1 Score

Accuracy

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Project Phase - 2: Train Facial Emotion Recognition

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Gather Emotion Images

Data Preprocessing

Train Machine Learning Models

Tuning Machine Learning Models

Model Evaluation

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Project Phase -3: Django Web App Developed in Local (Computer)

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Setting Up Visual Studio Code

Install all Dependencies of VS Code

Setting Virtual Environment

Freeze Requirements

Learn Django Basics

SETTINGS

URLS

VIEWS

TEMPLATES (HTML)

Face Recognition Django Project

Models Views Templates (MVT)

Design SQLite Database in Django

Store Uploaded Image in Database

Integrate Machine Learning to Django

MVT + Machine Learning Framework

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Styling Django Web App with Bootstrap

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Project Phase -4: Deploy Web App in Heroku Cloud for Production

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Setting up Heroku Account.

Creating App in Heroku

Install Heroku CLI, GIT

Deploy Heroku in Cloud

Necessary Installation to Fix CSS in Heroku.

You will learn image processing techniques in OpenCV and the concepts behind the images. We will also do the necessary image analysis and required preprocessing steps for images.

For the preprocess images, we will extract features from the images using deep neural networks then with the features of faces, we will train the Machine learning model and also learn to test our model before deploying, to get the best results from the model we will tune with the Grid search method for the best hyperparameters.

Once our machine learning model is ready, will we learn and develop a web server gateway interphase in Django by rendering HTML CSS and bootstrap in the frontend and in the backend written in Python. Then, we will create the project on the Face Recognition project by integrating the machine learning model to Django App.

Finally we will deploy this entire Django Web App in Heroku Cloud for production and get URL/domain where you can access anywhere in the world.

We know that Face Recognition with Django and Deployed in Heroku leaves you will questions and error. Don't worry we are here are there to answer all the question and help you to complete the course.

Screenshots