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Computer Vision Projects with OpenCV and Python 3

You're reading from   Computer Vision Projects with OpenCV and Python 3 Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789954555
Length 182 pages
Edition 1st Edition
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Author (1):
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Matthew Rever Matthew Rever
Author Profile Icon Matthew Rever
Matthew Rever
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Table of Contents (9) Chapters Close

Preface 1. Setting Up an Anaconda Environment FREE CHAPTER 2. Image Captioning with TensorFlow 3. Reading License Plates with OpenCV 4. Human Pose Estimation with TensorFlow 5. Handwritten Digit Recognition with scikit-learn and TensorFlow 6. Facial Feature Tracking and Classification with dlib 7. Deep Learning Image Classification with TensorFlow 8. Other Books You May Enjoy

Facial recognition

We're going to see how we can perform facial recognition with dlib with a relatively small amount of code. Facial recognition here means that we're going to look at an image and see whether or not this person is the same as the person in a different image. We're going to keep it simple here and just compare two faces to see whether they're the same, but this can easily be generalized, as we'll see later.

Here, we're going to do something similar to the first example, where we're going to prompt the user to open two files, each with a face that is going to be compared to another. For this, we are going to use some faces from Labeled Faces in the Wild (LFW). It's a nice database that has thousands of faces from various celebrities. You can download the entire set from http://vis-www.cs.umass.edu/lfw/ and get a whole lot...

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