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
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
- Chapter 1 : Introduction
- Chapter 2 : Environment Setup: Using Anaconda Package
- Chapter 3 : Python Basics
- Chapter 4 : Setting Up Environment - Additional Dependencies (with DLib Fixes)
- Chapter 5 : Introduction to Face Detectors
- Chapter 6 : Face Detection Implementation
- Chapter 7 : Optional: cv2.imshow() Not Responding Issue Fix
- Chapter 8 : Real-Time Face Detection from Webcam
- Chapter 9 : Video Face Detection
- Chapter 10 : Real-Time Face Detection - Face Blurring
- Chapter 11 : Real-Time Facial Expression Detection - Installing Libraries
- Chapter 12 : Real-Time Facial Expression Detection - Implementation
- Chapter 13 : Video Facial Expression Detection
- Chapter 14 : Image Facial Expression Detection
- Chapter 15 : Real-Time Age and Gender Detection Introduction
- Chapter 16 : Real-Time Age and Gender Detection Implementation
- Chapter 17 : Image Age and Gender Detection Implementation
- Chapter 18 : Introduction to Face Recognition
- Chapter 19 : Face Recognition Implementation
- Chapter 20 : Real-Time Face Recognition
- Chapter 21 : Video Face Recognition
- Chapter 22 : Face Distance
- Chapter 23 : Face Landmarks Visualization
- Chapter 24 : Multi Face Landmarks
- Chapter 25 : Face Makeup Using Face Landmarks
- Chapter 26 : Real-Time Face Makeup
- Title: Computer Vision: Face Recognition Quick Starter in Python
- Release date: July 2020
- Publisher(s): Packt Publishing
- ISBN: 9781800567221
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