Created by Jones Granatyr | Last updated 2/2021
Duration: 1h 55m | 1 sections | 19 lectures | Video: 1280x720, 44 KHz | 952 MB
Genre: eLearning | Language: English + Sub
Learn how to detect faces from images and videos using Python, OpenCV and Dlib!
What you'll learn
Understand the basic intuition about Cascade and HOG classifiers to detect faces
Implement face detection using OpenCV and Dlib library
Learn how to detect other objects using OpenCV, such as cars, clocks, eyes, and full body of people
Compare the results of three face detectors: Haarcascade, HOG (Histogram of Oriented Gradients) and CNN (Convolutional Neural Networks)
Detect faces using images and the webcam
Requirements
Basic Python programming
Description
Face detection is one of the sub-areas of Computer Vision that aims to detect people's faces in images or videos. Smartphones and digital cameras use these features to improve the quality of photos by placing a rectangle around the faces of the detected people. 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. In this context, it is important that the system knows how to differentiate a person from a cat, so that the alarm does not ring unnecessarily.
In this free course we will use the Python language, OpenCV and Dlib library, which are one of the most used today for digital image processing. You will learn step by step how to detect people's faces in images and also in the webcam! In addition, we will have a bonus in which we will implement the detection of other objects, such as eyes, cars, clocks and even the full body of people! We will send a picture and the algorithm will identify the presence of these objects! The good point is that OpenCV and Dlib have native features for this task, which makes development quite fast and with few lines of code! Below you can see some of the topics you are going to learn:
You are going to learn the basic intuition of the following algorithms: Haarcascade, HOG (Histograms of Oriented Gradients) and CNN (Convolutional Neural Networks). Also, we will perform some tests in order to compare the results of each one, so you can see what is the best algorithm to be applied to your custom projects.
Who this course is for:
Bners who are starting to learn Computer Vision
Undergraduate students who are studying subjects related to Artificial Intelligence
People who want to solve their own problems using Computer Vision
Students who want to work in companies developing Computer Vision projects
People who want to know all areas inside Computer Vision, as well as know the problems that these techniques are able to solve
Anyone interested in Artificial Intelligence or Computer Vision
Data scientists who want to grow their portfolio
Professionals who want to understand how to apply Computer Vision to real projects
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