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Machine Learning Solutions

You're reading from   Machine Learning Solutions Expert techniques to tackle complex machine learning problems using Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788390040
Length 566 pages
Edition 1st Edition
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Author (1):
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Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
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Table of Contents (19) Chapters Close

Machine Learning Solutions
Foreword
Contributors
Preface
1. Credit Risk Modeling 2. Stock Market Price Prediction FREE CHAPTER 3. Customer Analytics 4. Recommendation Systems for E-Commerce 5. Sentiment Analysis 6. Job Recommendation Engine 7. Text Summarization 8. Developing Chatbots 9. Building a Real-Time Object Recognition App 10. Face Recognition and Face Emotion Recognition 11. Building Gaming Bot List of Cheat Sheets Strategy for Wining Hackathons Index

Understanding the concepts of face recognition


In this section, we will look at the major concepts of face recognition. These concepts will include the following topics:

  • Understanding the face recognition dataset

  • The algorithm for face recognition

Understanding the face recognition dataset

You may wonder why I haven't discussed anything related to the dataset until now. This is because I don't want to confuse you by providing all the details about the datasets of two different applications. The dataset that we will cover here is going to be used for face recognition.

If you want to build a face recognition engine from scratch, then you can use following datasets:

  • CAS-PEAL Face Dataset

  • Labeled Faces in the Wild

Let's discuss them in further detail.

CAS-PEAL Face Dataset

This is a huge dataset for face recognition tasks. It has various types of face images. It contains face images with different sources of variations, especially Pose, Emotion, Accessories, and Lighting (PEAL) for face recognition tasks...

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