Deploy Face Recognition Project With Python, Django, And Machine Learning

Deploy Face Recognition Web App, Machine Learning, Django, and database in Heroku Cloud with this comprehensive course.

One of the most often utilized technologies in the fields of artificial intelligence and data science is computer vision and facial recognition. To create an end-to-end data science application, it is crucial to have expert-level knowledge in machine learning and deep learning, in addition to having an understanding of web development.

This course is a one-stop course where you will learn End to End development of a Computer-Vision Artificial Intelligence Project from scratch. 

Deploy Face Recognition Project Overview

The course will walk you through the construction of an end-to-end project. I will begin the course by installing Python and the required libraries in Python. Next, I'll teach you OpenCV image processing algorithms and the mathematical principles underlying the images, as one of the course's requirements. Also, we will do the essential picture analysis and image pretreatment procedures. Next, using OpenCV and Deep Neural Networks, we will complete a mini-project on Face Detection.

We will next begin our project's first phase, facial identification recognition, using the fundamental notions of images. Beginning with picture preprocessing, we will use deep neural networks to extract features from the photos. Then, using the facial features, we will train various machine learning models including random forest, logistic regression, and support vector machines. Then, we incorporate a voting classifier into all machine learning models (stacking method). I'll show you how to choose a model and adjust a facial recognition model's hyperparameters.

We will use machine learning methods for recognizing facial emotions in Phase 2 in addition to face identity detection. Afterward, we'll create a pipeline by fusing together all the various detection and recognition models.

When our machine learning model is complete, we will go on to Phase 3 and create a web application in Django that renders HTML, CSS, and Bootstrap on the front end while utilizing Python on the back end. I'll go over the Django prerequisites in this section. Finally, with the MVT (Models, Views, and Templates) framework, we will create a web application. In order to create the Django App, we will first create a SQLite database. I'll also show you how to integrate machine learning pipeline models with the MVT framework in this section. Finally, we'll use Bootstrap to style our app.

Lastly, we'll publish the full Django Web App on the Heroku Cloud, giving you a URL or domain to access it from anywhere in the globe. In addition, I'll walk you through every installation step and walk you through troubleshooting any difficulties you run into while deploying your web app.

Why are you holding out? Develop your own Computer Vision Django Web Project using Python and Machine Learning to start the course, then manually deploy it to the cloud.

Curriculum breakdown:

As this course is a full-stack course we designed this course into 4 phases

Phase- 1: Machine Learning - Face Identify Recognition

• In this phase, we majorly cover the practical concepts related to machine learning models like data preprocessing, analysis, training machine learning, and model evaluation and selection (Grid Search Hyperparameter Tuning)

• Here I will teach you how to develop face recognition models using machine learning

Phase- 2: Machine Learning - Facial Emotion Recognition

• Here we will develop another machine learning-based face recognition for facial emotion recognition.

Phase- 3: Django Web App Development

• In this phase, I will teach you how to develop a Web App with Django.

• We will use a powerful framework which is the MVT (Models Views Templates) framework to develop the web app.

• You will also learn how to design a database (SQLite) for the Web App in Django.

• Integrate Machine Learning Model to MVT framework

• I will also explain, styling using Bootstrap

Phase- 4: Deployment / Production

• In this phase, we will deploy the Django web app on a cloud platform which is the HEROKU cloud

• I will explain all the necessary steps and installation to deploy the Django Project

If you want to become an AI developer this is the perfect course to start with. Below given is the high-level abstract of the course and the learning objectives.

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

Project Phase - 1: Face Recognition and Person Identity

• 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 using the Ensemble Technique with a Voting Classifier

• Tuning Machine Learning Model

• Model Evaluation

• Precision

• Recall 

• Sensitivity

• Specificity

• F1 Score

• Accuracy

Project Phase - 2:  Train Facial Emotion Recognition

• Gather Emotion Images

• Data Preprocessing

• Train Machine Learning Models

• Tuning Machine Learning Models

• Model Evaluation

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

• 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

Styling Django Web App with Bootstrap

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

• Setting up Heroku Account.

• Creating an App in Heroku

• Install Heroku CLI, GIT

• Deploy Heroku in the Cloud

• Necessary Installation to Fix CSS in Heroku.