Course Information
Course Overview
SSD face and facial mask detection, and train your own model to recognize faces even with masks
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 Content
- 10 section(s)
- 54 lecture(s)
- Section 1 Set up the environment
- Section 2 Jupyter notebook coding environment
- Section 3 Image process
- Section 4 Classification model explanation
- Section 5 Tensorflow introduction and quick guide
- Section 6 Write a classification class program
- Section 7 FaceNet concepts
- Section 8 Create FaceNet model
- Section 9 Face alignment of CASIA dataset using SSD face detection
- Section 10 Face alignment of CASIA dataset using MTCNN
What You’ll Learn
- 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 time face detection, mask detection, and face recognition
Reviews
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NNuha Babakr
I am thankful to you johnny you did a great job .. you deserve every cent i payed for this course .. the courses are clear and explained well .. You are helpful and patiently answers all question i will not hesitate to buy another course with you ( i suggest GAN course for the same task Masked Face Recognition with face inpainting or rebuilding masked area)
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MMariam Aljaser
Everything is explained in quick and detailed, Its a clear explanation
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TTrondo Sheng
The lesson is quite fast pace, difficult to understand. I think it will be suitable for those who are experience in deep learning
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RRachmawan Atmaji Perdana
Installing Environment using Anaconda