Computer Vision: Face Recognition Quick Starter in Python

Video description

This course will help you delve into face recognition using Python without having to deal with all the complexities and mathematics associated with the deep learning process.

You will start with an introduction to face detection and face recognition technology. After this, you’ll get the system ready by installing the Anaconda package and other dependencies and libraries. You’ll then write Python code to detect faces from a given image and extract the faces as separate images. Next, you’ll focus on face detection by streaming a real-time video from the webcam.

Customize the face detection program to blur the detected faces dynamically from the webcam video stream. You’ll also learn facial expression recognition and age and gender prediction using a pre-trained deep learning model.

Later, you’ll progress to writing Python code for face recognition, which will help identify the faces that are already detected. Then you’ll explore the concept of face distance and tweak the face landmark points used for face detection.

By the end of this course, you’ll be well-versed with face recognition and detection and be able to apply your skills in the real world.

What You Will Learn

  • Become well-versed with face detection and face recognition technology
  • Understand how to install the Anaconda package
  • Install dependencies and libraries such as dlib, OpenCV, and Pillow
  • Learn how to perform face detection and face recognition
  • Use the face distance parameter to calculate the magnitude of faces
  • Create custom face make-up for an image with face landmark points

Audience

This course is designed for beginners or anyone who wants to get started with Python-based face recognition.

About The Author

Abhilash Nelson: Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than 8 years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.

Table of contents

  1. Chapter 1 : Introduction
    1. Course Introduction and Table of Contents
    2. Introduction to Face Recognition
  2. Chapter 2 : Environment Setup: Using Anaconda Package
    1. Environment Setup: Using Anaconda Package
  3. Chapter 3 : Python Basics
    1. Python Basics - Assignment
    2. Python Basics - Flow Control
    3. Python Basics - Data Structures
    4. Python Basics - Functions
  4. Chapter 4 : Setting Up Environment - Additional Dependencies (with DLib Fixes)
    1. Setting Up Environment - Additional Dependencies - Part 1
    2. Setting Up Environment - Additional Dependencies - Part 2
    3. (Optional) DLib Error: Downgrading Python and Fixing
  5. Chapter 5 : Introduction to Face Detectors
    1. Introduction to Face Detectors
  6. Chapter 6 : Face Detection Implementation
    1. Face Detection Implementation Part 1
    2. Face Detection Implementation
  7. Chapter 7 : Optional: cv2.imshow() Not Responding Issue Fix
    1. Optional: cv2.imshow() Not Responding Issue Fix
  8. Chapter 8 : Real-Time Face Detection from Webcam
    1. Real-Time Face Detection - Part 1
    2. Real-Time Face Detection - Part 2
  9. Chapter 9 : Video Face Detection
    1. Video Face Detection
  10. Chapter 10 : Real-Time Face Detection - Face Blurring
    1. Real-Time Face Detection - Face Blurring
  11. Chapter 11 : Real-Time Facial Expression Detection - Installing Libraries
    1. Real-Time Facial Expression Detection - Installing Libraries
  12. Chapter 12 : Real-Time Facial Expression Detection - Implementation
    1. Real-Time Facial Expression Detection - Implementation Part 1
    2. Real-time Facial Expression Detection - Implementation Part 2
  13. Chapter 13 : Video Facial Expression Detection
    1. Video Facial Expression Detection
  14. Chapter 14 : Image Facial Expression Detection
    1. Image Facial Expression Detection
  15. Chapter 15 : Real-Time Age and Gender Detection Introduction
    1. Real-Time Age and Gender Classification
  16. Chapter 16 : Real-Time Age and Gender Detection Implementation
    1. Real-Time Age and Gender Detection Implementation
  17. Chapter 17 : Image Age and Gender Detection Implementation
    1. Image Age and Gender Detection Implementation
  18. Chapter 18 : Introduction to Face Recognition
    1. Introduction to Face Recognition
  19. Chapter 19 : Face Recognition Implementation
    1. Face Recognition Implementation - Part 1
    2. Face Recognition Implementation - Part 2
  20. Chapter 20 : Real-Time Face Recognition
    1. Real-Time Face Recognition - Part 1
    2. Face Recognition Implementation - Part 2
  21. Chapter 21 : Video Face Recognition
    1. Video Face Recognition
  22. Chapter 22 : Face Distance
    1. Face Distance - Part 1
    2. Face Distance - Part 2
  23. Chapter 23 : Face Landmarks Visualization
    1. Face Landmarks Visualization - Part 1
    2. Face Landmarks Visualization - Part 2
  24. Chapter 24 : Multi Face Landmarks
    1. Multi Face Landmarks
    2. Multi Face Landmarks from a Real-Time and Pre-Saved Video
  25. Chapter 25 : Face Makeup Using Face Landmarks
    1. Face Makeup Using Face Landmarks
  26. Chapter 26 : Real-Time Face Makeup
    1. Real-Time Face Makeup

Product information

  • Title: Computer Vision: Face Recognition Quick Starter in Python
  • Author(s): Abhilash Nelson
  • Release date: July 2020
  • Publisher(s): Packt Publishing
  • ISBN: 9781800567221