The Ultimate Beginners Guide to Facial Detection Recognition
Learn the differences between face detection and recognition, detect and recognize faces using OpenCV and Dlib libraries, and evaluate algorithms for the best results. Perfect for AI students, data scientists, and beginners in Computer Vision.
What you’ll 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
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
User Reviews
Be the first to review “The Ultimate Beginners Guide to Facial Detection Recognition”
You must be logged in to post a review.
There are no reviews yet.