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?
Image Processing with OpenCV
Image Data Preprocessing
Image Data Analysis
Eigenfaces with PCA
Face Recognition Classification Model with Support Vector Machines
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.
Who this course is for:
Any one who want to learn image processing and build data science applications
Beginners on Python who want to data science project
Who want to start their career in artificial intelligence and data science
Data science beginner who want to build end to end data science project
8 sections • 69 lectures • 9h 27m total length
Download all Resourses
Walk through on Jupyter Notebook
Data Type Casting
User Defined Functions
Control Statements (if else)
Range & Zip
Understanding Image Pixels - Part 1
Understanding Image Pixels - Part 2
Automatic Face Object Detection
Working on Videos
Machine Learning Pipeline Architecture
Data Preprocessing - Crop Faces from Data using OpenCV
Data Preprocessing - Face Size Info with Pandas
Data Preprocessing - EDA with Pandas
Data Preprocessing - Structure the Faces with OpenCV
Data Preprocessing - Normalization
Feature Extraction - PCA (Eigen Faces)
Feature Extraction - PCA (Eigen Faces) - part2
Machine Learning - SVM based Face Recognition Model
Sudhir is an experienced Data Scientist with a demonstrated history of working in the information technology and services industry. Skilled in Machine Learning, Deep Learning, Statistical algorithms he mostly worked on Image Processing and Natural Language processing application. He also successfully deployed many data science-related projects in cloud platforms as a service. Strong engineering professional with a Bachelor's degree focused on Electrical and Electronics Engineering.
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