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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

Requirements

  • Programming logic
  • Basic Python programming

Description

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!

Who this course is for

  • 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

Ratings and Reviews

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Review posted on Udemy
Posted 1 month ago
by Emmi Lajunen

Extremely satisfied with my good course completion. discovered fresh information. Everyone could understand the instructor's explanation, which was simple to follow.

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Review posted on Udemy
Posted 1 month ago
by Eugenia Spencer

Wonderful course. Excellent justifications and visuals. Incredibly comprehensive!

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Posted 1 month ago
by Monica Uppa

Fantastic course, exactly what I needed, many thanks.

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Review posted on Udemy
Posted 1 month ago
by Allan Howell

Most recent lecture and tasks are simple to understand.

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Review posted on Udemy
Posted 1 month ago
by Gilbert Crist

It was really good! I gained a lot of knowledge that will undoubtedly aid in my skill development!

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Review posted on Udemy
Posted 3 months ago
by Elias Mckinney

Excellent, just what I'm searching for, and I received a very fast answer to my request. I sincerely appreciate your effort.

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Review posted on Udemy
Posted 3 months ago
by Dale Anderson

Extremely nicely produced and displayed. My excitement is building to finish it.

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Review posted on Udemy
Posted 3 months ago
by Charles Garrison

The latest tutorial and tasks are simple to understand.

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Review posted on Udemy
Posted 3 months ago
by Stephen Gibson

Fantastic course! Very well explained and interesting. Strongly advised!

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Review posted on Udemy
Posted 3 months ago
by Bella Smith

The most effective way to detect and identify faces.

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