Course description
The Ultimate Beginners Guide to Face Detection & Recognition is a complete beginner-level course to learn facial recognition techniques using OpenCV and Dlib libraries in Python. Face recognition is a sub-section of computer vision that aims to recognize people’s faces in images or videos. Smartphones and digital cameras use these features to select people in the photo, usually placing a rectangle around the face. This type of application has found significant relevance in security systems, where it is necessary to determine whether people are present in the environment to activate an alarm. On the other hand, facial recognition aims to recognize people’s faces, and one of the examples is security systems that can use these features to identify the presence or absence of a person in an environment. It is important to highlight the differences between face detection and face recognition techniques: while the former only shows the presence of a face, the latter shows whose face has been detected.
What you will learn in this course:
- Learn the difference between Face Detection and Face Recognition
- Face detection using Haarcascade, HOG (Histogram of Oriented Gradients), MMOD (Maximum Margin Object Detection) and SSD (Single Shot Multiple Box Detection)
- Recognize faces in images, videos and webcams using OpenCV and Dlib libraries
- Recognition of faces using Eigenfaces, Fisherfaces, LBPH (Histrograms Patterns Local Binary Patterns) and advanced Deep Learning techniques
- Evaluate facial recognition algorithms to choose the best one for your application
Who is this course suitable for?
- People who are 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
- Beginners in Computer Vision
Course specifications :
Headlines of the 2023/4 period:
Course prerequisites:
- Programming logic
- Basic Python programming
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