Face Recognition with Machine Learning + Deploy Flask App

Name: Face Recognition with Machine Learning + Deploy Flask App
Rating: 4.5 (497 reviews)

Create an Face Recognition project from scratch with Python, OpenCV , Machine Learning Algorithms, Flask, Heroku Deploy

Created bydatascience Anywhere, G Sudheer, VedicSkill Academy

Last updated 9/2023

English

What you'll learn

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
Deploy Flask App in Heroku Cloud

Course content

10 sections • 115 lectures • 10h 47m total length

Introduction2:36
Face Recognition Project Components1:42
The components that are used to develop the face recognition projects is Machine Learning, Python, OpenCV, Numpy, Pandas, Sklearn, Flask, HTML & CSS
Download all Resourses0:02
Install Python2:23
Clone Face Recognition Template0:44
Create and Install Virtual Environment & Packages3:12
Next step0:14
Install and verify the necessary packages from the requirements file, then explore the foundations of Open CV in the next lesson; see you there and happy learning.

Face Recognition Model Introduction2:17
Download Resources0:02
About Data2:30
In this course we are using IMDB Wiki data for developing Face recognition model. However it is highly recommended to download the given in the resources.
Face Recognition Training Flow2:38
This lecture explains the training architecture for the face recognition model.
Data Gathering
Data Preprocessing
Train ML model
Create ML Pipeline
Data Preprocess : Idea of Detect & Crop Face2:04
Data Preprocessing: Get Data1:50
Data Preprocessing: Import Required Libraries1:54
Data Preprocess: Get List of path of all Images2:47
Data Preprocess: Detect Face with Haar Cascade Classifier8:28
Data Preprocess: Crop Detected Face2:10
Data Preprocess: Crop All Faces8:47
Data Preprocess: Idea of Structuring Images4:21
Data Preprocessing: Structuring Data Part - 110:14
Data Preprocessing: Exploratory Data Analysis11:24
Perform exploratory data analysis of gender and image size distributions using bar, pie, box, and histogram plots. Propose resizing to 100 by 100 to balance data for preprocessing.
Data Preprocessing: Filter Low Resolution Images and Resize images8:21
Data Preprocessing: Structure all Images7:41
Eigen Face: Flow1:20
Eigen: Mean Face and PCA12:09
Eigen Face: Get Optimal components for PCA8:47
Visualize explained variance and cumulative variance from PCA to determine the optimal number of components, using an elbow plot to select around 50 components for 80% explained variance.
Eigen Face: Save PCA ML model5:26
Eigen Face: Visualize Eigen Face4:21
Train Face Recognition Model Part - 13:01
Train a face recognition model using a support vector machine, with grid search cross-validation for hyperparameter tuning, and save results after data preprocessing and feature extraction.
Train Face Recognition Model - Part 24:21
Train Face Recognition Model Part - 36:56
Best Estimator2:06
Model Evaluation8:09
Save Face Recognition Model2:00
Face Recognition Pipeline part 15:46
Face Recognition Pipeline part 27:24
Face Recognition Pipeline part 38:12
Face Recognition Pipeline part 49:37
Face Recognition Pipeline part 55:30
Predictions part -14:27
Predictions part -25:16
Predictions part -37:43

Introduction0:53
Installing Flask and Visual Studio Code7:35
Your First Flask App3:49
Flask Routing6:19
URL Building7:54
Flask Jinja Templates - Part 18:15
Flask Jinja Templates - Part 212:32
Flask Jinja Templates - Part 314:01
Template Inheritance10:55
Static Files ( CSS, JS)9:37
Learn to manage static CSS and JavaScript in a Flask app by creating static/css and static/js folders, linking files with url_for, and applying styles to HTML.
Http Methods in Flask14:27
File Upload in Flask17:12

Requirements

Should be at-least beginner level in Python
Be able to understand HTML and CSS
Basic Understanding of Machine Learning Concepts

Description

MLOPs: AI based Face Recognition Web App in Flask & Deploy

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)
Develop Face Recognition Web
Deploy Flask App in Cloud (Heroku)

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

Face Recognition with Machine Learning + Deploy Flask App

What you'll learn

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Course content

Introduction7 lectures • 11min

Image Processing with OpenCV13 lectures • 1hr 15min

Develop Face Recognition Model with Machine Learning from Scratch35 lectures • 3hr 10min

Face Recognition Project (Integrating HTML Model to Flask App)16 lectures • 1hr 28min

Deploy Web App in Heroku Cloud7 lectures • 32min

Deploying in another open-course cloud.3 lectures • 11min

Appendix - Python Crash Course19 lectures • 2hr 7min

[Optional]: Flask Crash Course12 lectures • 1hr 53min

Extra Tips2 lectures • 1min

Bonus Lecture1 lecture • 1min

Requirements

Description

Who this course is for: