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The Ultimate Beginners Guide to Face Detection & Recognition

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The Ultimate Beginners Guide to Face Detection & Recognition, available at $69.99, has an average rating of 4.64, with 63 lectures, based on 63 reviews, and has 775 subscribers.

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You will learn about Learn the differences between face detection and face recognition Detect faces using Haarcascade, HOG (Histogram of Oriented Gradients), MMOD (Max-Margin Object Detection), and SSD (Single Shot Multibox Detector) Detect and recognize faces in images, videos and from the webcam using OpenCV and Dlib libraries Recognize faces using Eigenfaces, Fisherfaces, LBPH (Local Binary Patterns Histrograms), and advanced Deep Learing techniques Evaluate face recognition algorithms in order to choose the best one according to your application This course is ideal for individuals who are People interested in face detection and face recognition using OpenCV and Dlib libraries or Undergraduate and graduate students who are taking courses related to Artificial Intelligence or Data Scientists who want to increase their project portfolio or Beginners in Computer Vision It is particularly useful for People interested in face detection and face recognition using OpenCV and Dlib libraries or Undergraduate and graduate students who are taking courses related to Artificial Intelligence or Data Scientists who want to increase their project portfolio or Beginners in Computer Vision.

Enroll now: The Ultimate Beginners Guide to Face Detection & Recognition

Summary

Title: The Ultimate Beginners Guide to Face Detection & Recognition

Price: $69.99

Average Rating: 4.64

Number of Lectures: 63

Number of Published Lectures: 63

Number of Curriculum Items: 63

Number of Published Curriculum Objects: 63

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

  • Learn the differences between face detection and face recognition
  • Detect faces using Haarcascade, HOG (Histogram of Oriented Gradients), MMOD (Max-Margin Object Detection), and SSD (Single Shot Multibox Detector)
  • Detect and recognize faces in images, videos and from the webcam using OpenCV and Dlib libraries
  • Recognize faces using Eigenfaces, Fisherfaces, LBPH (Local Binary Patterns Histrograms), and advanced Deep Learing techniques
  • Evaluate face recognition algorithms in order to choose the best one according to your application

Who Should Attend

  • People interested in face detection and face recognition using OpenCV and Dlib libraries
  • Undergraduate and graduate students who are taking courses related to Artificial Intelligence
  • Data Scientists who want to increase their project portfolio
  • Beginners in Computer Vision

Target Audiences

  • People interested in face detection and face recognition using OpenCV and Dlib libraries
  • Undergraduate and graduate students who are taking courses related to Artificial Intelligence
  • Data Scientists who want to increase their project portfolio
  • Beginners in Computer Vision

Facial detection is a subarea of Computer Vision that aims to detect people’s faces in images or videos. Smartphones and digital cameras use these features to select people in a photo, usually placing a rectangle around the face. This type of application has gained considerable relevance in security systems, in which it is necessary to identify whether there are people in an environment for the alarm to be triggered. On the other hand, facial recognition aims to recognize people’s faces and one example is security systems that can use these features to identify whether or not a person is present in an environment. It is important to highlight the differences between face detection and recognition techniques: while the first only indicates if a face is present, the second indicates whose face is detected.

In this step by step course using Python programming language, you are going to learn how to detect and recognize faces from images, videos and webcam from the most basic to the most advanced techniques!See below the topics that you be covered:

  • Detection of faces using Haarcascade, HOG (Histogram of Oriented Gradients), MMOD (Max-Margin Object Detection), and SSD (Single Shot Multibox Detector)

  • Detection of other objects, such as eyes, smiles, clocks, bodies, and cars

  • Recognition of faces using Eigenfaces, Fisherfaces, LBPH (Local Binary Patterns Histograms), and advanced Deep Learning techniques

  • How to compare the performance of the algorithms

  • Build your custom dataset capturing faces via webcam

All implementations will be done step by step using Google Colab online, so you do not need to worry about installing and configuring the tools on your own machine! More than 60 lectures and 8 hours of step by step videos!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course content

Lecture 2: Detection vs recognition

Lecture 3: Course materials

Chapter 2: Face detection

Lecture 1: OpenCV and Dlib

Lecture 2: Images and pixels

Lecture 3: Haarcascade – intuition

Lecture 4: Loading the image

Lecture 5: Face detection with haarcascade

Lecture 6: Resizing the image

Lecture 7: Haarcascade parameters 1

Lecture 8: Haarcascade parameters 2

Lecture 9: Eye detection

Lecture 10: Detection of smiles, clocks, body, and cars

Lecture 11: HOG (Histogram of Oriented Gradients) – intuition

Lecture 12: Face detection with HOG

Lecture 13: Upsampling parameter

Lecture 14: Max-Margin Object Detection (MMOD) – intuition

Lecture 15: Face detection with MMOD

Lecture 16: Single Shot MultiBox Detector (SSD) – intuition

Lecture 17: Face detection with SSD 1

Lecture 18: Face detection with SSD 2

Lecture 19: HOMEWORK

Lecture 20: Homework solution

Lecture 21: Face detection in videos 1

Lecture 22: Face detection in videos 2

Lecture 23: Face detection in videos 3

Lecture 24: Face detection in videos 4

Lecture 25: Face detection in videos 5

Lecture 26: Installing Anaconda and PyCharm

Lecture 27: Webcam face detection

Chapter 3: Face recognition

Lecture 1: Eigenfaces – intuition

Lecture 2: Yalefaces dataset

Lecture 3: Eigenfaces – implementation 1

Lecture 4: Eigenfaces – implementation 2

Lecture 5: Eigenfaces – implementation 3

Lecture 6: Eigenfaces – implementation 4

Lecture 7: Eigenfaces – implementation 5

Lecture 8: Fisherfaces – intuition

Lecture 9: Fisherfaces – implementation

Lecture 10: LBPH – intuition

Lecture 11: LBPH – implementation 1

Lecture 12: LBPH – parameters

Lecture 13: LBPH – implementation 2

Lecture 14: Deep learning with Dlib 1

Lecture 15: Deep learning with Dlib 2

Lecture 16: Deep learning with Dlib 3

Lecture 17: Deep learning with Dlib 4

Lecture 18: Deep learning with Dlib 5

Lecture 19: Deep learning with Dlib 6

Lecture 20: Face recognition library 1

Lecture 21: Face recognition library 2

Lecture 22: Face recognition library 3

Lecture 23: Face recognition library 4

Lecture 24: HOMEWORK

Lecture 25: Homework solution

Lecture 26: Face recognition library 5

Lecture 27: Face alignment and face elements

Lecture 28: Video face recognition

Lecture 29: Project: capturing faces via webcam

Lecture 30: Project: traditional algorithms

Lecture 31: Project: deep learning algorithms

Chapter 4: Final remarks

Lecture 1: Final remarks

Lecture 2: BONUS

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 0 votes
  • 3 stars: 5 votes
  • 4 stars: 17 votes
  • 5 stars: 40 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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