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Learning OpenCV 3 Computer Vision with Python (Update)

You're reading from   Learning OpenCV 3 Computer Vision with Python (Update) Unleash the power of computer vision with Python using OpenCV

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Product type Hardcover
Published in Jun 2026
Publisher
ISBN-13 9781803230221
Length
Edition 1st Edition
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Authors (2):
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Joe Minichino Joe Minichino
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Joe Minichino
Joseph Howse Joseph Howse
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Joseph Howse
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Table of Contents (12) Chapters Close

1. Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning FREE CHAPTER
2. Setting Up OpenCV 3. Handling Files, Cameras, and GUIs 4. Processing Images with OpenCV 5. Detecting and Recognizing Faces 6. Retrieving Images and Searching Using Image Descriptors 7. Building Custom Object Detectors 8. Tracking Objects 9. Camera Models and Augmented Reality 10. Introduction to Neural Networks with OpenCV 11. OpenCV Applications at Scale Appendix A: Bending Color Space with the Curves Filter

Performing face recognition

Detecting faces is a fantastic feature of OpenCV and one that constitutes the basis for a more advanced operation: face recognition. What is face recognition? It is the ability of a program, given an image or a video feed containing a person's face, to identify that person. One of the ways to achieve this (and the approach adopted by OpenCV) is to train the program by feeding it a set of classified pictures (a facial database) and perform recognition based on the features of those pictures.

Another important feature of OpenCV's face recognition module is that each recognition has a confidence score, which allows us to set thresholds in real-life applications to limit the incidence of false identifications.

Let's start from the very beginning; to perform face recognition, we need faces to recognize. We fulfill this requirement in two ways: supply the images ourselves or obtain freely available face databases. A large directory of face databases...

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