Deep Learning- masked face detection, recognition

Deep Learning: masked face detection, recognition, available at $119.99, has an average rating of 4.2, with 55 lectures, based on 49 reviews, and has 225 subscribers.

You will learn about How to install Python, Tensorflow, Pycharm from scratch How to create your own classification model Whats FaceNet Whats the difference between classification models and face recognition models How to create your own FaceNet model by modifying the classification model How to do the face alignment using SSD face detection How to do the face alignment using MTCNN face detection How to do the data cleaning How to create masked face dataset How to train your FaceNet model What are training skills How to implement training skills to train models effectively How to perform the real time face detection, mask detection, and face recognition This course is ideal for individuals who are Those who have Python basics tend to learn Deep Learning or Face Recognition or Any engineers who want to level up in Deep Learning It is particularly useful for Those who have Python basics tend to learn Deep Learning or Face Recognition or Any engineers who want to level up in Deep Learning.

Enroll now: Deep Learning: masked face detection, recognition

Summary

Title: Deep Learning: masked face detection, recognition

Price: $119.99

Average Rating: 4.2

Number of Lectures: 55

Number of Published Lectures: 54

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 54

Original Price: $119.99

Quality Status: approved

Status: Live

What You Will Learn

How to install Python, Tensorflow, Pycharm from scratch

How to create your own classification model

Whats FaceNet

Whats the difference between classification models and face recognition models

How to create your own FaceNet model by modifying the classification model

How to do the face alignment using SSD face detection

How to do the face alignment using MTCNN face detection

How to do the data cleaning

How to create masked face dataset

How to train your FaceNet model

What are training skills

How to implement training skills to train models effectively

How to perform the real time face detection, mask detection, and face recognition

Who Should Attend

Those who have Python basics tend to learn Deep Learning or Face Recognition

Any engineers who want to level up in Deep Learning

Target Audiences

Those who have Python basics tend to learn Deep Learning or Face Recognition

Any engineers who want to level up in Deep Learning

Deep Learning of artificial intelligence(AI) is an exciting future technology with explosive growth.

Masked face recognition is a mesmerizing topic which contains several AI technologies including classifications, SSD object detection, MTCNN, FaceNet, data preparation, data cleaning, data augmentation, training skills, etc.

Nowadays, people are required to wear masks due to the COVID-19 pandemic.

The conventional FaceNet model barely recognizes faces without masks

Even the FaceID on iPhone or iPad devices only works without masks.

In this course, I will teach you how to train a model that works with masks.

In the final presentation, you will be able to perform the real time face detection, face mask detection, and face recognition, even with masks!

Windows is the operating system so you don’t need to learn Linux first.

Having Python and Tensorflow knowledge are required.

In my tutorials, I would like to explain difficult theories and formulas by easy concepts or practical examples.

Model training always takes a lot of time.

Take this project as an example, it needs more than 400,000 images to train.

I will offer training skills to speed up the training process.

These training skills can be not only applied in face recognition but also in your future projects.

All lectures are spoken in plain English.

If you feel my speaking pace is quite slow, you can use the gear setting to speed up.

If you don’t want to train the model by yourself, the source code and trained weight files are included!

Besides the training steps, this is also a highly integrated application.

Achievement from the topic, skills grow from the project. I hope you enjoy the fun of AI.

Course Curriculum

Chapter 1: Set up the environment

Lecture 1: Environment installation

Chapter 2: Jupyter notebook coding environment

Lecture 1: Jupyter notebook

Lecture 2: How to use Jupyter notebook

Chapter 3: Image process

Lecture 1: Lecture_1

Lecture 2: Lecture_2

Lecture 3: Lecture_3

Lecture 4: How to distribute the dataset into train and test set

Chapter 4: Classification model explanation

Lecture 1: What is a classification model

Lecture 2: Recall, precision, and accuracy

Lecture 3: Elements of a classification model

Chapter 5: Tensorflow introduction and quick guide

Lecture 1: Introduction_1

Lecture 2: Introduction_2

Lecture 3: Introduction_3

Lecture 4: Introduction_4

Lecture 5: Introduction_5

Lecture 6: Lets write codes

Chapter 6: Write a classification class program

Lecture 1: Overview of classification model

Lecture 2: Class initialization

Lecture 3: Model initialization

Lecture 4: Model train method

Lecture 5: Save CKPT, PB files and log files

Chapter 7: FaceNet concepts

Lecture 1: Concepts_1

Lecture 2: Concepts_2

Chapter 8: Create FaceNet model

Lecture 1: An introduction of Inception ResNet V1

Lecture 2: Create FaceNet model by modifying the classification model

Chapter 9: Face alignment of CASIA dataset using SSD face detection

Lecture 1: Concepts

Lecture 2: Introduction of SSD face detection method

Lecture 3: Write the program

Chapter 10: Face alignment of CASIA dataset using MTCNN

Lecture 1: Concepts and write codes

Chapter 11: CASIA data cleaning

Lecture 1: Concepts

Lecture 2: cleaning function introduction

Lecture 3: cleaning function explanation_1

Lecture 4: cleaning function explanation_2

Lecture 5: cleaning function explanation_3

Lecture 6: cleaning function explanation_4

Lecture 7: cleaning function explanation_5

Lecture 8: cleaning function explanation_6

Chapter 12: Create a dataset with facial masks

Lecture 1: Explanation of this method

Lecture 2: Lets write codes

Lecture 3: Test the program

Chapter 13: Train FaceNet model

Lecture 1: Modify the codes of inputs

Lecture 2: Real training is started

Lecture 3: Modify the codes of check results

Chapter 14: Training skills

Lecture 1: Concepts and code explanation

Lecture 2: Modify the codes of get_4D_data

Chapter 15: Evaluation of recognizing faces with facial masks

Lecture 1: Concepts, write codes and get the accuracy

Chapter 16: Training skills episode 2

Lecture 1: Concepts and code explanation

Lecture 2: Modify the class

Lecture 3: Results and discussion

Chapter 17: Real time face detection, facial mask detection, and face recognition

Lecture 1: Explanation_1

Lecture 2: Explanation_2

Lecture 3: Lets write codes

Lecture 4: Use a laptop to perform the real time face recognition

Chapter 18: How to train a smaller model

Lecture 1: Master and apprentice method

Instructors

Johnny Liao
AI computer vision algorithm engineer

Rating Distribution

1 stars: 2 votes

2 stars: 2 votes

3 stars: 4 votes

4 stars: 13 votes

5 stars: 28 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!