Computer Vision: Face Recognition Quick Starter in Python

Description

This course is designed for beginners or anyone who wants to get started with Python-based face recognition. In this course, you will learn how to perform face detection from images, face detection from real-time videos, emotion detection, age-gender prediction, face recognition from images and real-time videos, and more!

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. All the codes and supporting files for this course will be available at https://github.com/PacktPublishing/Computer-Vision-Face-Recognition-Quick-Starter-in-Python

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

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

Starting with an introductory section along with plenty of examples, this course will help you even if you do not have a Python background by getting you up and running with the basics. You'll be able to learn effectively with detailed explanations and even useful Python assignments.

Use Python to detect and recognize faces from images and real-time webcam video * Become well-versed with emotion detection * Get up to speed with predicting age and gender from images and real-time webcam video

https://github.com/PacktPublishing/Computer-Vision-Face-Recognition-Quick-Starter-in-Python

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.

1. Introduction to face recognition

2. Introduction to Face Recognition

3. Environment Setup: Installing Anaconda Package

4. Python Basics (Optional)

5. Setting up Environment - Additional Dependencies (With DLib Fixes)

6. (Optional) DLib Error: Downgrading Python and Fixing

7. Introduction to Face Detectors

8. Face Detection Implementation

9. Optional: cv2.imshow() Not Responding Issue Fix

10. Realtime Face Detection from WebCam

11. Video Face Detection

12. Realtime Face Detection - Face Blurring

13. Real-time Facial Expression Detection - Installing Libraries

14. Real-time Facial Expression Detection - Implementation

15. Video Facial Expression Detection

16. Image Facial Expression Detection

17. Real-time Age and Gender Detection Introduction

18. Real-time Age and Gender Detection Implementation

19. Image Age and Gender Detection Implementation

20. Introduction to Face Recognition

21. Face Recognition Implementation

22. Realtime Face Recognition

23. Video Face Recognition

24. Face Distance

25. Face Landmarks Visualization

26. Multi Face Landmarks

27. Multi Face Landmarks from Real-time and Pre-saved Video

28. Face Makeup Using Face Landmarks

29. Real-time Face Makeup