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Hands-On Machine Learning with IBM Watson

You're reading from  Hands-On Machine Learning with IBM Watson

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789611854
Pages 288 pages
Edition 1st Edition
Languages
Author (1):
James D. Miller James D. Miller
Profile icon James D. Miller
Toc

Table of Contents (15) Chapters close

Preface 1. Section 1: Introduction and Foundation
2. Introduction to IBM Cloud 3. Feature Extraction - A Bag of Tricks 4. Supervised Machine Learning Models for Your Data 5. Implementing Unsupervised Algorithms 6. Section 2: Tools and Ingredients for Machine Learning in IBM Cloud
7. Machine Learning Workouts on IBM Cloud 8. Using Spark with IBM Watson Studio 9. Deep Learning Using TensorFlow on the IBM Cloud 10. Section 3: Real-Life Complete Case Studies
11. Creating a Facial Expression Platform on IBM Cloud 12. The Automated Classification of Lithofacies Formation Using ML 13. Building a Cloud-Based Multibiometric Identity Authentication Platform 14. Another Book You May Enjoy

Feature extraction

Biometric feature extraction (also sometimes named minutia extraction) refers to the process by which established key features of a sample are selected or enhanced for more efficient processing. Typically, the process of feature extraction relies on a set of algorithms that varies depending on the type (face image or fingerprints, for example) of biometric identification used.

Biometric authentication is the matching of samples that have been converted (previously or upon attempt) from, for example, an image of a biometric trait into a searchable set of data. This conversion is the process known as feature extraction.

If you look for example of how feature extraction fundamentally works, you see that it depends upon the type of sample, but is, for the most part, quite easy to conceptualize. You can head over to the following link to know more on how a biometric...

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