Computer Vision: Face Recognition Quick Starter in Python

Quickly Build Python Deep Learning based Face Detection, Recognition, Emotion , Gender and Age Classification Systems

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Resumen del curso

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.

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 a 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 they are 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 a 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 a line joining these face landmark 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 have been uploaded and shared in a folder.

¿Qué son los requerimientos?

  • A decent configuration computer and an enthusiasm to dive into the world of computer vision based Face Recognition

¿Qué voy a obtener de este curso?

  • Face Detection from Images, Face Detection from Realtime Videos, Emotion Detection, Age-Gender Prediction, Face Recognition from Images, Face Recognition from Realtime

¿Cuál es el público objetivo?

  • Beginners or who wants to start with Python based Face Recognition

Sobre el Autor

I  am a pioneering, talented and security-oriented Android/iOS Mobile and PHP/Python Web Developer Application Developer offering more than eight years’ overall IT experience which involves designing, implementing, integrating, testing and supporting impact-full web and mobile applications. I am a Post Graduate Masters Degree holder in Computer Science and Engineering. My experience with PHP/Python Programming is an added advantage for server based Android and iOS Client Applications. I am currently serving full time as a Senior Solution Architect managing my client's projects from start to finish to ensure high quality, innovative and functional design.

Curso curricular

Course Introduction and Table of Contents
1 Video Lectures | 00:06:53

  • Course Introduction and Table of Contents
    06:53
     

Introduction to Face Recognition
1 Video Lectures | 00:04:17

  • Introduction to Face Recognition
    04:17
     

IMPORTANT: Tensorflow Incompatibility
1 Document Lectures

  • Link To Download Python 3.7 version of Anaconda
    5 Page

Environment Setup: Instal ling Anaconda Package
1 Video Lectures | 00:07:48

  • Environment Setup: Installing Anaconda Package
    07:48
     

Python Basics(Optional)
4 Video Lectures | 00:33:44

  • Python Basics - Assignment
    08:34
     
  • Python Basics - Flow Control
    09:23
     
  • Python Basics - Data Structures
    11:51
     
  • Python Basics - Functions
    03:56
     

Setting up Environment - Additional Dependencies (With DLib Fixes)
2 Video Lectures | 00:20:33

  • Setting up Environment - Additional Dependencies - Part 1
    06:42
     
  • Setting up Environment - Additional Dependencies (With DLib Fixes) - Part 2
    13:51
     

(Optional) DLib Error : Downgrading Python and Fixing
1 Video Lectures | 00:03:12

  • (Optional) DLib Error : Downgrading Python and Fixing
    03:12
     

Introduction to Face Detectors
1 Video Lectures | 00:05:00

  • Introduction to Face Detectors
    05:00
     

Face Detection Implementation
2 Video Lectures | 00:13:47

  • Face Detection Implementation - Part 1
    06:32
     
  • Face Detection Implementation - Part 2
    07:15
     

Optional: cv2.imshow() Not Responding Issue Fix
1 Video Lectures | 00:01:18

  • Optional: cv2.imshow() Not Responding Issue Fix
    01:18
     

Realtime face detection from WebCam
2 Video Lectures | 00:17:57

  • Realtime face detection - Part 1
    09:35
     
  • Realtime face detection - Part 2
    08:22
     

Video Face Detection
1 Video Lectures | 00:02:44

  • Video Face Detection
    02:44
     

Realtime face detection - Face Blurring
1 Video Lectures | 00:04:06

  • Realtime face detection - Face Blurring
    04:06
     

Real-time Facial Expression Detection - Installing Libraries
1 Video Lectures | 00:07:28

  • Real-time Facial Expression Detection - Installing Libraries
    07:28
     

Real-time Facial Expression Detection - Implementation
1 Document Lectures | 2 Video Lectures | 00:14:35

  • Real-time Facial Expression Detection - Implementation - Part 1
    08:59
     
  • Real-time Facial Expression Detection - Implementation - Part 2
    05:36
     
  • TensorFlow "Module Not Found" Error Fix (Optional) - Do ONLY if you have error
    1 Page

Video Facial Expression Detection
1 Video Lectures | 00:01:49

  • Video Facial Expression Detection
    01:49
     

Image Facial Expression Detection
1 Video Lectures | 00:05:05

  • Image Facial Expression Detection
    05:05
     

Real-time Age and Gender Detection Introduction
1 Video Lectures | 00:05:06

  • Real-time Age and Gender Detection Introduction
    05:06
     

Real-time Age and Gender Detection Implementation
1 Video Lectures | 00:12:28

  • Real-time Age and Gender Detection Implementation
    12:28
     

Image Age and Gender Detection Implementation
1 Video Lectures | 00:03:45

  • Image Age and Gender Detection Implementation
    03:45
     

Introduction to Face Recognition
1 Video Lectures | 00:04:01

  • Introduction to Face Recognition
    04:01
     

Face Recognition Implementation
2 Video Lectures | 00:21:06

  • Face Recognition Implementation - Part 1
    10:02
     
  • Face Recognition Implementation - Part 2
    11:04
     

Realtime Face Recognition
2 Video Lectures | 00:11:36

  • Realtime Face Recognition - Part 1
    04:48
     
  • Realtime Face Recognition - Part 2
    06:48
     

Video Face Recognition
1 Video Lectures | 00:03:05

  • Video Face Recognition
    03:05
     

Face Distance
2 Video Lectures | 00:11:37

  • Face Distance - Part 1
    05:00
     
  • Face Distance - Part 2
    06:37
     

Face Landmarks Visualization
2 Video Lectures | 00:12:50

  • Face Landmarks Visualization - Part 1
    03:49
     
  • Face Landmarks Visualization - Part 2
    09:01
     

Multi Face Landmarks
1 Video Lectures | 00:04:37

  • Multi Face Landmarks
    04:37
     

Multi Face Landmarks from Real-time and Pre-saved Video
1 Video Lectures | 00:06:55

  • Multi Face Landmarks from Real-time and Pre-saved Video
    06:55
     

Face Makeup Using Face Landmarks
1 Video Lectures | 00:07:28

  • Face Makeup Using Face Landmarks
    07:28
     

Real-time Face Makeup
1 Video Lectures | 00:03:19

  • Real-time Face Makeup
    03:19
     

SOURCE CODE AND FILES ATTACHED
1 Document Lectures

  • SOURCE CODE AND FILES ATTACHED
    49 Page

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