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Face Recognition A-Z: Hands-On Build Train & Deploy

Develop & Deploy Face Recognition, Facial Emotion using OpenCV, Machine Learning, Django & Database in Python in Heroku

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Welcome to the Course Deploy Face Recognition Web App, Machine Learning, Django & Database in Heroku Cloud !!!.

An Artificial Intelligence Project.

Computer Vision & Face recognition is one of the most widely used in the area of Artificial Intelligence and Data Science. If at all you want to develop an end-to-end application in Data Science, then you need to be a master in Machine Learning / Deep Learning, and in addition to that, you need to have knowledge in Web Development.

This course is one stop course where you will learn End to End development of a Computer-Vision Based Artificial Intelligence Project from SCRATCH. As this course is a full-stack course we designed this course into 4 phases

  • Phase-1: Machine Learning - Face Identify Recognition

  • Phase-2: Machine Learning - Facial Emotion Recognition

  • Phase-3: Django Web App Development

  • Phase-4: Deployment / Production


I will start the course by installing Python and installing the necessary libraries in Python for developing the end-to-end project. Then I will teach you one of the prerequisites of the course that is image processing techniques in OpenCV and the mathematical concepts behind the images. We will also do the necessary image analysis and required preprocessing steps for the images. Then we will do a mini project on Face Detection using OpenCV and Deep Neural Networks.

With the concepts of image basics, we will then start our project phase-1, face identity recognition. I will start this phase with preprocessing images, we will extract features from the images using deep neural networks. Then with the features of faces, we will train the different Machine learning models like logistic regression, support vector machines, random forest. Then we combine all machine learning models with Voting Classifier (stacking method). I will teach you the model selection and hyperparameter tuning for face recognition models

In Phase-2, we will apply the machine learning techniques used in face identity recognition for facial emotion recognition. After that, we will combine all different detection and recognition models into a pipeline.

Once our machine learning model is ready, will we move to Phase-3, and develop a Web Application in Django by rendering HTML CSS and bootstrap in the frontend and in the backend written in Python. Here I will teach you the necessary prerequisite of Django. Then we will develop a web app using the MVT (Models, Views, and Templates) framework. We will start developing Django App by designing a database in SQLite. Here I will also teach you to interphase machine learning pipeline models to the MVT framework. In the end, we will style our app using Bootstrap.

Finally, we will deploy the entire Django Web App in Heroku Cloud for production and get a URL/domain where you can access it anywhere in the world. I will also teach all the necessary installation required and explain how to solve errors whenever you have encountered them while deploying your web app.

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

What you will learn?

Prerequisite of Project: OpenCV

  1. Image Processing with OpenCV

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

  3. Feature Extraction with OpenCV and Deep Learning Networks

Project Phase - 1: Face Recognition and Person Identity

  1. Gather Images

  2. Extract Faces only from Images

  3. Labeling (Target output) Images

  4. Data Preprocessing

  5. Training Face Recognition with OWN Machine Learning Models.

  6. Combine All Machine Learning Models using Ensemble Technique with Voting Classifier

  7. Tuning Machine Learning Model

  8. Model Evaluation

Project Phase - 2: Train Facial Emotion Recognition

  1. Gather Emotion Images

  2. Data Preprocessing

  3. Train Machine Learning Models

  4. Tuning Machine Learning Models

  5. Model Evaluation

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

  1. Setting Up Visual Studio Code

  2. Install all Dependencies of VS Code

  3. Setting Virtual Environment

  4. Freeze Requirements

  5. Learn Django Basics


    2. URLS



  6. Face Recognition Django Project

    1. Models Views Templates (MVT)

  7. Design SQLite Database in Django

  8. Store Uploaded Image in Database

  9. Integrate Machine Learning to Django

    1. MVT + Machine Learning Framework

Styling Django Web App with Bootstrap

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

  1. Setting up Heroku Account.

  2. Creating App in Heroku

  3. Install Heroku CLI, GIT

  4. Deploy Heroku in Cloud

  5. Necessary Installation to Fix CSS in Heroku.

What are you waiting for? Start the course develop your own Computer Vision Django Web Project using Machine Learning, Python and Deploy it in Cloud with your own hands.

I will see you inside the course.

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