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Python Machine Learning Cookbook

You're reading from   Python Machine Learning Cookbook Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789808452
Length 642 pages
Edition 2nd Edition
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Authors (2):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Toc

Table of Contents (18) Chapters Close

Preface 1. The Realm of Supervised Learning FREE CHAPTER 2. Constructing a Classifier 3. Predictive Modeling 4. Clustering with Unsupervised Learning 5. Visualizing Data 6. Building Recommendation Engines 7. Analyzing Text Data 8. Speech Recognition 9. Dissecting Time Series and Sequential Data 10. Analyzing Image Content 11. Biometric Face Recognition 12. Reinforcement Learning Techniques 13. Deep Neural Networks 14. Unsupervised Representation Learning 15. Automated Machine Learning and Transfer Learning 16. Unlocking Production Issues 17. Other Books You May Enjoy

Recognizing faces using the HOG-based model

By face recognition, we mean the process that returns the position of the faces that are present in an image. In the Building a face detector using Haar cascades recipe, we already addressed this topic. In this recipe, we will use the face_recognition library to perform a series of operations on these faces.

The focal objective of face recognition consists of detecting the characteristics of a face and ignoring everything else that surrounds it. This is a feature on multiple commercial devices, and it allows you to establish when and how to apply focus in an image so that you can capture it. In the world of computer vision, it is customary to divide the family of face detection algorithms into two major categories. What distinguishes these two categories is their different uses of information, derived from a priori knowledge of the...

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