This plan includes
- Limited free courses access
- Play & Pause Course Videos
- Video Recorded Lectures
- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Email & Chat Support
What you'll learn?
- Face Detection from Images, Face Detection from Realtime Videos, Emotion Detection, Age-Gender Prediction, Face Recognition from Images, Face Recognition from Realtime Videos, Face Distance, Face Landmarks Manipulation, Face Makeup. . Also includes a Python basics refresher session.
Course Overview
Hi There!
welcome to my new course 'Face Recognition with Deep Learning using Python'. This is the second course from my Computer Vision series.
Face Detection and Face Recognition is the most used applications of Computer Vision. Using these techniques, the computer will be able to extract one or more faces in an image or video and then compare it with the existing data to identify the people in that image.
Face Detection and Face Recognition is widely used by governments and organizations for surveillance and policing. We are also making use of it daily in many applications like face unlocking of cell phones etc.
This course will be a quick starter for people who wants to dive deep into face recognition using Python without having to deal with all the complexities and mathematics associated with typical Deep Learning process.
We will be using a python library called face-recognition which uses simple classes and methods to get the face recognition implemented with ease. We are also using OpenCV, Dlib and Pillow for python as supporting libraries.
Let's now see the list of interesting topics that are included in this course.
At first we will have an introductory theory session about Face Detection and Face Recognition technology.
After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package. Then we will install the rest of dependencies and libraries that we require including the dlib, face-recognition, opencv etc and will try a small program to see if everything is installed fine.
Most of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions and data structures.
Then we will have an introduction to the basics and working of face detectors which will detect human faces from a given media. We will try the python code to detect the faces from a given image and will extract the faces as separate images.
Then we will go ahead with face detection from a video. We will be streaming the real-time live video from the computer's webcam and will try to detect faces from it. We will draw rectangle around each face detected in the live video.
In the next session, we will customize the face detection program to blur the detected faces dynamically from the webcam video stream.
After that we will try facial expression recognition using pre-trained deep learning model and will identify the facial emotions from the real-time webcam video as well as static images
And then we will try Age and Gender Prediction using pre-trained deep learning model and will identify the Age and Gender from the real-time webcam video as well as static images
After face detection, we will have an introduction to the basics and working of face recognition which will identify the faces already detected.
In the next session, We will try the python code to identify the names of people and their the faces from a given image and will draw a rectangle around the face with their names on it.
Then, like as we did in face detection we will go ahead with face recognition from a video. We will be streaming the real-time live video from the computer's webcam and will try to identify and name the faces in it. We will draw rectangle around each face detected and beneath that their names in the live video.
Most times during coding, along with the face matching decision, we may need to know how much matching the face is. For that we will get a parameter called face distance which is the magnitude of matching of two faces. We will later convert this face distance value to face matching percentage using simple mathematics.
In the coming two sessions, we will learn how to tweak the face landmark points used for face detection. We will draw line joining these face land mark points so that we can visualize the points in the face which the computer is used for evaluation.
Taking the landmark points customization to the next level, we will use the landmark points to create a custom face make-up for the face image.
That's all about the topics which are currently included in this quick course. The code, images and libraries used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked.
Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio.
So that's all for now, see you soon in the class room. Happy learning and have a great time.
Pre-requisites
- A decent configuration computer and an enthusiasm to dive into the world of computer vision based Face Recognition
Target Audience
- Beginners or who wants to start with Python based Face Recognition
Curriculum 34 Lectures 03:54:01
-
Section 1 : Course Introduction and Table of Contents
-
Section 2 : Introduction to Face Recognition
- Lecture 1 :
- Introduction to Face Recognition
-
Section 3 : Environment Setup: Installing Anaconda Package
- Lecture 1 :
- Environment Setup: Installing Anaconda Package
-
Section 4 : Python Basics (Optional)
- Lecture 1 :
- Python Basics - Assignment
- Lecture 2 :
- Python Basics - Flow Control
- Lecture 3 :
- Python Basics - Data Structures
- Lecture 4 :
- Python Basics - Functions
-
Section 5 : Setting up Environment - Additional Dependencies (With DLib Fixes)
- Lecture 1 :
- Setting up Environment - Additional Dependencies - Part 1
- Lecture 2 :
- Setting up Environment - Additional Dependencies - Part 2
-
Section 6 : (Optional) DLib Error : Downgrading Python and Fixing
- Lecture 1 :
- (Optional) DLib Error : Downgrading Python and Fixing
-
Section 7 : Introduction to Face Detectors
- Lecture 1 :
- Introduction to Face Detectors
-
Section 8 : Face Detection Implementation
- Lecture 1 :
- Face Detection Implementation - Part 1
- Lecture 2 :
- Face Detection Implementation - Part 2
-
Section 9 : Realtime face detection from WebCam
- Lecture 1 :
- Realtime face detection - Part 1
- Lecture 2 :
- Realtime face detection - Part 2
-
Section 10 : Realtime face detection - Face Blurring
- Lecture 1 :
- Realtime face detection - Face Blurring
-
Section 11 : Real-time Facial Expression Detection - Installing Libraries
- Lecture 1 :
- Real-time Facial Expression Detection - Installing Libraries
-
Section 12 : Real-time Facial Expression Detection - Implementation
- Lecture 1 :
- Real-time Facial Expression Detection - Implementation - Part 1
- Lecture 2 :
- Real-time Facial Expression Detection - Implementation - Part 2
-
Section 13 : Image Facial Expression Detection
- Lecture 1 :
- Image Facial Expression Detection
-
Section 14 : Real-time Age and Gender Detection Introduction
- Lecture 1 :
- Real-time Age and Gender Detection Introduction
-
Section 15 : Real-time Age and Gender Detection Implementation
- Lecture 1 :
- Real-time Age and Gender Detection Implementation
-
Section 16 : Image Age and Gender Detection Implementation
- Lecture 1 :
- Image Age and Gender Detection Implementation
-
Section 17 : Introduction to Face Recognition
- Lecture 1 :
- Introduction to Face Recognition
-
Section 18 : Face Recognition Implementation
- Lecture 1 :
- Face Recognition Implementation - Part 1
- Lecture 2 :
- Face Recognition Implementation - Part 2
-
Section 19 : Realtime Face Recognition
- Lecture 1 :
- Realtime Face Recognition - Part 1
- Lecture 2 :
- Realtime Face Recognition - Part 2
-
Section 20 : Face Distance
- Lecture 1 :
- Face Distance - Part 1
- Lecture 2 :
- Face Distance - Part 2
-
Section 21 : Face Landmarks Visualization
- Lecture 1 :
- Face Landmarks Visualization - Part 1
- Lecture 2 :
- Face Landmarks Visualization - Part 2
-
Section 22 : Face Makeup Using Face Landmarks
- Lecture 1 :
- Face Makeup Using Face Landmarks
-
Section 23 : SOURCE CODE AND FILES ATTACHED
- Lecture 1 :
- SOURCE CODE AND FILES ATTACHED
Our learners work at
Frequently Asked Questions
-
How do i access the course after purchase?
It's simple. When you sign up, you'll immediately have unlimited viewing of thousands of expert courses, paths to guide your learning, tools to measure your skills and hands-on resources like exercise files. There’s no limit on what you can learn and you can cancel at any time. -
Are these video based online self-learning courses?
Yes. All of the courses comes with online video based lectures created by certified instructors. Instructors have crafted these courses with a blend of high quality interactive videos, lectures, quizzes & real world projects to give you an indepth knowledge about the topic. -
Can i play & pause the course as per my convenience?
Yes absolutely & thats one of the advantage of self-paced courses. You can anytime pause or resume the course & come back & forth from one lecture to another lecture, play the videos mulitple times & so on. -
How do i contact the instructor for any doubts or questions?
Most of these courses have general questions & answers already covered within the course lectures. However, if you need any further help from the instructor, you can use the inbuilt Chat with Instructor option to send a message to an instructor & they will reply you within 24 hours. You can ask as many questions as you want. -
Do i need a pc to access the course or can i do it on mobile & tablet as well?
Brilliant question? Isn't it? You can access the courses on any device like PC, Mobile, Tablet & even on a smart tv. For mobile & a tablet you can download the Learnfly android or an iOS app. If mobile app is not available in your country, you can access the course directly by visting our website, its fully mobile friendly. -
Do i get any certificate for the courses?
Yes. Once you complete any course on our platform along with provided assessments by the instructor, you will be eligble to get certificate of course completion. -
For how long can i access my course on the platform?
You require an active subscription to access courses on our platform. If your subscription is active, you can access any course on our platform with no restrictions. -
Is there any free trial?
Currently, we do not offer any free trial. -
Can i cancel anytime?
Yes, you can cancel your subscription at any time. Your subscription will auto-renew until you cancel, but why would you want to?
Instructor
282252 Course Views
19 Courses