Deep Learning in Practice III: Face Recognition
Deep Learning in Practice III: Face Recognition, available at $49.99, has an average rating of 4.5, with 39 lectures, based on 57 reviews, and has 288 subscribers.
You will learn about Recognize the fundamentals of face recognition systems Extract a face using MTCNN in Python Create the face embedding using FaceNet in Tensorflow and Keras Identify the identity of a person from his face This course is ideal for individuals who are Beginner users who would like to get quickly started with face recognition systems. It is particularly useful for Beginner users who would like to get quickly started with face recognition systems.
Enroll now: Deep Learning in Practice III: Face Recognition
Summary
Title: Deep Learning in Practice III: Face Recognition
Price: $49.99
Average Rating: 4.5
Number of Lectures: 39
Number of Published Lectures: 39
Number of Curriculum Items: 40
Number of Published Curriculum Objects: 40
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Recognize the fundamentals of face recognition systems
- Extract a face using MTCNN in Python
- Create the face embedding using FaceNet in Tensorflow and Keras
- Identify the identity of a person from his face
Who Should Attend
- Beginner users who would like to get quickly started with face recognition systems.
Target Audiences
- Beginner users who would like to get quickly started with face recognition systems.
About the course
Welcome to the course Deep Learning in Practice III on Face Recognition. I am Anis Koubaa, and I will be your instructor in this course.
This course is the third course in the series Deep Learning in Practice. It provides a fast and easy-to-follow introduction to face recognition with deep learning using MTCNN for face extraction and FaceNet for face recognition. My two previous courses deal with object classification and transfer learning with Tensorflow and Keras.
In this course, you will learn the whole loop of face recognition systems, which starts by extracting the face from an image and localizing the face in an image by its bounding box; then, we process the extracted face through a convolutional neural network, called FaceNet in our case, to create a fingerprint of the face, which we call face embedding. The face embedding can be stored in a database so that they are compared with other face embeddings to identify the person of interest.
In this course, you will have a step-by-step introduction to this whole loop, and I will show you how you can develop a Python application that performs the abovementioned operations. Exciting, right?
Why is the course important?
This course is essential due to the importance of face recognition systems in real-world applications. These fast-growing systems are used in several applications, such as surveillance systems, face access systems, and biometric identification.
In this course, you will be introduced to face recognition systems both from a theoretical and practical perspective, allowing you to develop your own projects using face recognition in Python.
The course’s motivation is a lack of resources to get quickly started with the topic. So taking this course will save you tons of time looking for scattered references over the Internet and will get you much quicker into the field.
What’s worth?
This course provides fast yet comprehensive coverage of face recognition systems that would let you go from Zero to Hero.
I first start with presenting the fundamental concepts of face recognition systems and how deep learning models for face embedding are trained and produced.
Then, I provide a hands-on introduction to face recognition using MTCCN for face extraction and FaceNet for face recognition, all with Python programming language. Tensorflow and Keras APIs will be used to load the FaceNet model. I provide a Jupiter notebook that you will use as a guide in the lecture to follow and write the code to apply as you learn.
At the end of this course, I guarantee that you will understand the whole loop of face recognition systems, and you will be able to develop your application and integrate it into your project.
Pre-requisites
To benefit from this course most, you just need to know about Python programming.
Having a basic understanding of deep learning and TensorFlow would be a plus, but it is not mandatory.
In any case, you may refer to my two courses: Deep Learning in Practice I and II, for a basic practical introduction to deep learning.
Welcome to the course, and I wish you a pleasant learning experience.
Let’s get started.
About me
I am Anis Koubaa, and I am working as a Full Professor in Computer Science and Leader of the Robotics and Internet-of-Things Lab at Prince Sultan University
I am the author of two best-seller courses on Deep Learning and Robot Operating System (ROS),
and this course is the third course in the series Deep Learning in Practice, which deals with face recognition systems.
The series of deep learning in practice intends to present advanced deep learning topics very easily to beginner users who would like to get started with hands-on projects in deep learning in a minimum amount of time.
The two previous courses dealt with object classification and transfer learning projects.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Course Overview
Lecture 2: Course Google Colab Notebooks
Lecture 3: [VERY IMPORTANT] MUST READ ABOUT WORKING ENVIRONMENT
Lecture 4: [IMPORTANT] What is you face errors and need to debug and find solutions?
Chapter 2: Face Recognition: Concepts and Theoretical Background
Lecture 1: Learning outcomes
Lecture 2: Demo
Lecture 3: Face Recognition vs Face Verification
Lecture 4: Difference with Traditional Classification
Lecture 5: Face Recognition Use Case
Lecture 6: One Shot Classification and Siamese Networks
Lecture 7: One Shot Learning
Lecture 8: What is a Face Embedding?
Lecture 9: One Shot Learning Example
Lecture 10: Siamese Networks
Lecture 11: Create a Face Embedding with a Triplet Loss Training
Lecture 12: How to Build a Triplet Loss Dataset
Lecture 13: Illustration of Training a Siamese Network Model with Triplet Loss
Lecture 14: Triplet Loss Summary
Lecture 15: Face Recognition and Binary Classification
Chapter 3: Hands-on I: Create a face embedding using FaceNet in TF Keras
Lecture 1: Overview and Learning Outcomes
Lecture 2: Face Extraction and Face Recognition Background
Lecture 3: Install Dependencies and Import Libraries
Lecture 4: Load of a face image
Lecture 5: Extract Faces: MTCNN Library
Lecture 6: Face Metadata
Lecture 7: Understand the Face Bounding Box Coordinates
Lecture 8: Analyze the First Detected Face
Lecture 9: Analyze the Second Detected Face: The hidden face 🙂
Lecture 10: Extract the Face Bounding Box
Lecture 11: Face Embedding Pre-Requisites
Lecture 12: Load FaceNet with Tensorflow Keras Library
Lecture 13: Create a Face Embedding with FaceNet
Lecture 14: Generate and Understand the Face Embedding with FaceNet
Lecture 15: Save the Face Embedding in a File (create a face database)
Lecture 16: Load the Face Embedding from a File
Lecture 17: Concluding remarks
Chapter 4: Hands-on II: Recognize a face in an image
Lecture 1: Overview
Lecture 2: Extract Faces and Create Embeddings
Lecture 3: Face Identification using Face Embedding
Chapter 5: Develop your face recognition system
Instructors
-
Anis Koubaa
Professor of Computer Science
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 2 votes
- 3 stars: 11 votes
- 4 stars: 13 votes
- 5 stars: 30 votes
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