SSD face and facial mask detection, MTCNN face detection, and train your own model to recognize faces even with masks

How to install Python, Tensorflow, Pycharm from scratch

How to create your own classification model

What's FaceNet

What's 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 face detection, mask detection, and face recognition

High school mathematics level

Basic Python and Tensorflow

Desktop or laptop with at least 6GB Nvidia GPU cards

A USB camera

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 pand.

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 very proudly perform the real face detection, face mask detection, and face recognition, even with masks!

Windows is the operating system so you will not need to learn Linux first.

I will instruct you to install Python, Tensorflow, and Pycharm IDE from scratch.

Having basic Python and Tensorflow knowledge will be an advantage, but if not, I will have a quick guide to both for you.

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 .

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

I've spent much on training skills.

After numerous expental confirmation, I summarized my methods by plots to let you digest easily.

These training skills can be not only applied in face recognition but also in other models.

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.

The auto-generated captions may have errors. I will review and correct them.

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

Anyone interested in Deep Learning or Face Recognition

Any students in university who tend to start a project in Deep Learning

Any eeers who want to level up in Deep Learning



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