Deep Learning: masked face detection, recognition
SSD face and facial mask detection, and train your own model to recognize faces even with masks
Ratings 4.27 / 5.00
What You Will 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
Description
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.
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
Tags
- Deep Learning
- Python
- TensorFlow
- Face Detection
Subscribers
220
Lectures
54
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