Face Recognition Web App with Machine Learning in Flask
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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?
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Python
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Image Processing with OpenCV
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Image Data Preprocessing
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Image Data Analysis
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Eigenfaces with PCA
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Face Recognition Classification Model with Support Vector Machines
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Pipeline Model
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Flask (Jinja Template, HTML, CSS, HTTP Methods)
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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.
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IntroductionThis video walks through the course and explain the aim of the course and what you are develop by end of the course
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Face Recognition Project Components
The components that are used to develop the face recognition projects is Machine Learning, Python, OpenCV, Numpy, Pandas, Sklearn, Flask, HTML & CSS
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Download all Resourses -
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Installing Python
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Install and Create Virtual Environment
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Installing OpenCV and Dependencies
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Introduction
Walkthrough on essential OPENCV concepts required to develop a face recognition project.
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Understanding Images
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Display Images and Depth in Image
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Understanding Image Pixels - Part 1
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Understanding Image Pixels - Part 2
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Image Resizing
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Automatic Face Object Detection
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Working on Videos
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Machine Learning Pipeline Architecture
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Data Understanding
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Data Preprocessing - Crop Faces from Data using OpenCV
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Data Preprocessing - Face Size Info with Pandas
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Data Preprocessing - EDA with Pandas
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Data Preprocessing - Structure the Faces with OpenCV
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Data Preprocessing - Normalization
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Feature Extraction - PCA (Eigen Faces)
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Feature Extraction - PCA (Eigen Faces) - part2
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Machine Learning - SVM based Face Recognition Model
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Machine Learning - Model Evaluation
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Machine Learning - Grid Search (Tuning Model)
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Machine Learning - Grid Search (Tuning Model) - part2
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Machine Learning - Pipeline the Flow of Models
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Introduction
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Installing Flask and Visual Studio Code
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Your First Flask App
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Flask Routing
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URL Building
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Flask Jinja Templates - Part 1
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Flask Jinja Templates - Part 2
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Flask Jinja Templates - Part 3
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Template Inheritance
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Static Files ( CSS, JS)
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Http Methods in Flask
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File Upload in Flask
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Download the Resources
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Walk through on Jupyter Notebook
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Print Statements
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Escape and Insert keys
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Variables & Assignments
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Data Types
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Data Type Casting
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List
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List Methods
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Tuple
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Sets
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Dictionaries
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in operator
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plus operator
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User Defined Functions
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Control Statements (if else)
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Range & Zip
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For Loop
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Python Reference
