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.