Create an Face Recognition (AI) project from scratch with Python, OpenCV , Machine Learning Algorithms and Flask
☑ Automatic Face Recognition in images and videos
☑ Automatically detect faces from images and videos
☑ Evaluate and Tune Machine Learning
☑ Building Machine Learning Model for Classification
☑ Make Pipeline Model for deploying your application
☑ Image Processing with OpenCV
☑ Data Preprocessing for Images
☑ Create REST APIs in Flask
☑ Template Inheritance in Flask
☑ Integrating Machine Learning Model in Flask App
Face Recognition Web Project using Machine Learning in Flask Python
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?
- Python
- Image Processing with OpenCV
- Image Data Preprocessing
- Image Data Analysis
- Eigenfaces with PCA
- Face Recognition Classification Model with Support Vector Machines
- Pipeline Model
- Flask (Jinja Template, HTML, CSS, HTTP Methods)
- Finally, Face recognition Web App
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, ie. computing Eigen images using principal component analysis. With Eigen images, 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 flask by rendering HTML CSS and bootstrap in the frontend and in the backend written in Python. Finally, we will create the project on the Face Recognition project by integrating the machine learning model to Flask App.
English
Language
Introduction
Introduction
Installing Python
Install and Create Virtual Environment
Installing OpenCV and Dependencies
Python Refresher Sheet
Image Processing with OpenCV
Introduction
Understanding Images
Display Images and Depth in Image
Understanding Image Pixels – Part 1
Understanding Image Pixels – Part 2
Image Resizing
Object Detection
Working on Videos
Build Face Recognition Model with Machine Learning
Introduction
Machine Learning Pipeline Architecture
Data Understanding
Crop Faces from Image Data
Dealing with Unstructured Data (Faces) – part1
Dealing with Unstructured Data (Data Analysis) – part2
Dealing with Unstructured Data – part3
Data Preprocessing
Eigen Faces with Principal Component Analysis – part1
Eigen Faces with PCA – part2
Train Eigen Faces with Machine Learning Model
Model Evaluation
Tuning Machine Learning Model – part1
Tuning Machine Learning Model – part2
Make Pipeline Model (all together)
Flask App
Introduction
Installing Flask and Visual Studio Code
Your First Flask App
Flask Routing
URL Building
Flask Templates – Part 1
Flask Templates – Part 2
Flask Templates – Part 3
Template Inheritance
Static Files
Http Methods in Flask
File Upload in Flask
Face Recognition Project (Integrating HTML Model to Flask App)
Face Recognition Project Overview
Build Base HTML Part-1
Build Base HTML Part-2
Face App Page
Gender Classification Page – Part 1
Gender Classification Page – Part 2
Integrating Machine Learning Model to Flask App
BONUS
Deploying a Flask Application to Heroku