Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Machine Learning with IBM Watson

You're reading from   Hands-On Machine Learning with IBM Watson Leverage IBM Watson to implement machine learning techniques and algorithms using Python

Arrow left icon
Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789611854
Length 288 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction and Foundation FREE CHAPTER
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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image