This comprehensive course guides you through the fascinating world of face recognition using Python. Starting with an introduction to face recognition concepts, you'll proceed to set up your environment using Anaconda and address any initial setup challenges. The course then delves into Python basics, ensuring you have the foundational knowledge required for more advanced topics.
As you progress, you'll learn to implement face detection and recognition using the face_recognition and OpenCV libraries. The course covers real-time face detection from a webcam, video face detection, and various methods to handle common issues like cv2.imshow() not responding. By the end, you'll explore advanced topics such as facial expression detection, age, and gender classification, and even face makeup using face landmarks, solidifying your understanding and practical skills in computer vision.
This course is ideal for beginners interested in computer vision and face recognition. A basic understanding of programming concepts is recommended but not required, as the course covers Python basics. Whether you're aiming to enhance your technical skills or embark on a new career path, this course provides the tools and knowledge you need to succeed in the field of face recognition and computer vision.
Computer Vision: Face Recognition Quick Starter in Python
Overview
Syllabus
- Introduction
- In this module, we will introduce the course, providing an overview of the topics to be covered, and discuss the significance of face recognition in various applications. We'll also present the structure and objectives of the course to set clear expectations.
- Environment Setup: Using Anaconda Package
- In this module, we will set up the development environment by installing the Anaconda package. This will prepare our computer for Python coding, ensuring that we have the necessary tools and libraries for face recognition tasks.
- Python Basics
- In this module, we will cover essential Python programming basics, including assignments, flow control, data structures, and functions. This foundational knowledge is crucial for understanding and implementing face recognition algorithms.
- Setting Up Environment - Additional Dependencies (with DLib Fixes)
- In this module, we will install the necessary dependencies and libraries required for face recognition. We will also address common issues with DLib and ensure the environment is correctly configured for our projects.
- Introduction to Face Detectors
- In this module, we will introduce face detectors, discussing their importance and the different techniques used for detecting faces. This knowledge is fundamental for implementing effective face recognition solutions.
- Face Detection Implementation
- In this module, we will implement face detection in code using the face_recognition and OpenCV libraries. We will cover practical coding examples and ensure a thorough understanding of face detection implementation.
- Optional: cv2.imshow() Not Responding Issue Fix
- In this module, we will address the common issue of the cv2.imshow() function not responding while displaying images. We will implement a fix and verify that the display window functions correctly.
- Real-Time Face Detection from Webcam
- In this module, we will detect and locate faces from a real-time webcam video feed. We will cover the steps required to implement and optimize real-time face detection for practical applications.
- Video Face Detection
- In this module, we will detect and locate faces in pre-recorded video files. We will discuss the implementation details and performance considerations for video-based face detection.
- Real-Time Face Detection - Face Blurring
- In this module, we will blur detected faces in real-time video to ensure privacy. We will cover the implementation and testing of face blurring techniques in a real-time context.
- Real-Time Facial Expression Detection - Installing Libraries
- In this module, we will install the libraries required for real-time facial expression detection. Proper installation and configuration are essential for the subsequent implementation of facial expression detection.
- Real-Time Facial Expression Detection - Implementation
- In this module, we will detect facial expressions from a real-time webcam video feed. We will implement the necessary algorithms and optimize the detection process for accurate and efficient performance.
- Video Facial Expression Detection
- In this module, we will delve into the techniques for detecting facial expressions in video footage. We will explore methods to identify and analyze emotions based on facial cues, and implement algorithms that enhance the accuracy of facial expression recognition.
- Image Facial Expression Detection
- In this module, we will detect facial expressions in static images. We will discuss the implementation and validation of image-based facial expression detection techniques.
- Real-Time Age and Gender Detection Introduction
- In this module, we will introduce age and gender detection, discussing their significance and applications. We will provide an overview of the steps involved in implementing real-time age and gender classification.
- Real-Time Age and Gender Detection Implementation
- In this module, we will perform real-time age and gender classification on webcam video feed. We will focus on the implementation, optimization, and validation of the detection algorithms.
- Image Age and Gender Detection Implementation
- In this module, we will classify the age and gender of faces in static images. We will cover the implementation and validation of image-based detection algorithms.
- Introduction to Face Recognition
- In this module, we will introduce face recognition, discussing its applications and underlying principles. We will also address the challenges and solutions involved in face recognition technology.
- Face Recognition Implementation
- In this module, we will implement face recognition algorithms to detect and recognize faces in images. We will cover the coding and optimization techniques required for an effective face recognition system.
- Real-Time Face Recognition
- In this module, we will detect and recognize faces from a real-time webcam video feed. We will focus on implementing and optimizing real-time face recognition algorithms.
- Video Face Recognition
- In this module, we will detect and recognize faces in pre-recorded video files. We will discuss the implementation details and performance evaluation of video-based face recognition.
- Face Distance
- In this module, we will calculate the distance between faces for advanced analysis. We will cover the implementation and optimization of face distance algorithms.
- Face Landmarks Visualization
- In this module, we will learn how to visualize and customize face landmarks in images. We will cover the implementation and testing of face landmark visualization techniques.
- Multi Face Landmarks
- In this module, we will visualize and customize face landmarks for multiple faces in both real-time and pre-saved videos. We will focus on the implementation, optimization, and testing of multi-face landmark visualization techniques.
- Face Makeup Using Face Landmarks
- In this module, we will demonstrate how to customize face landmarks to apply simple makeup. We will cover the implementation and testing of face makeup techniques using face landmarks.
- Real-Time Face Makeup
- In this module, we will demonstrate face makeup in a real-time video using face landmarks. We will focus on implementing, optimizing, and validating real-time face makeup algorithms.
Taught by
Packt - Course Instructors
Related Courses
-
Computer Vision: Face Recognition Quick Starter in Python
-
Automated Multiple Face Recognition AI Using Python
-
Building a Face Detection and Recognition Model From Scratch
5.0 -
OpenCV Course - Full Tutorial with Python
4.4 -
OpenCV Tutorial - Develop Computer Vision Apps in the Cloud With Python
-
Computer Vision: YOLO Custom Object Detection with Colab GPU
