Explanation
The Ultimate Beginner’s Guide to Face Detection & Recognition is a complete beginner-level course to learn face recognition techniques using OpenCV and Dlib libraries in Python. Face recognition is a subfield of computer vision that aims to recognize human faces in photos or videos. Smartphones and digital cameras use these features to select the people in the picture, often placing rectangles in the face. This type of application has gained great importance in security systems, where it is necessary to determine whether people are in the area to trigger the alarm. On the other hand, facial recognition aims to recognize people’s faces, and for example, security systems can use these features to determine the presence and absence of a person in the environment. It is important to highlight the difference between face detection and face recognition techniques: while the former only shows the presence of a face, the latter shows its detected face.
What you will learn in this course:
- Learn the difference between Face Recognition and Face Recognition
- Face detection using Haarcascade, HOG (Histogram of Oriented Gradients), MMOD (Maximum Margin Object Detection) and SSD (Single Shot Multiple Box Detection)
- Discover and recognize faces from photos, videos and webcams using OpenCV and Dlib libraries
- Face recognition using Eigenfaces, Face-Fisher, LBPH (Historical Process of Local Binary Patterns) and advanced Deep Learning techniques
- Evaluate facial recognition algorithms to choose the best one for your application
Who is this course for:
- People interested in facial recognition and facial recognition using OpenCV and Dlib libraries
- Undergraduate and graduate students who are taking courses related to artificial intelligence
- Data scientists who want to grow their portfolio of projects
- Introduction to Computer Theory
Course specifications:
Course content 2023/4:
Requirements:
- program logic
- Basic Python programming
Images The Ultimate Beginner’s Guide to Face Detection & ID
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Installation guide:
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Quality: 720p
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File size:
3.05 GB