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
Mastering OpenCV with Practical Computer Vision Projects

You're reading from   Mastering OpenCV with Practical Computer Vision Projects This is the definitive advanced tutorial for OpenCV, designed for those with basic C++ skills. The computer vision projects are divided into easily assimilated chapters with an emphasis on practical involvement for an easier learning curve.

Arrow left icon
Product type Paperback
Published in Dec 2012
Publisher Packt
ISBN-13 9781849517829
Length 340 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Toc

Table of Contents (15) Chapters Close

Mastering OpenCV with Practical Computer Vision Projects
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Cartoonifier and Skin Changer for Android FREE CHAPTER 2. Marker-based Augmented Reality on iPhone or iPad 3. Marker-less Augmented Reality 4. Exploring Structure from Motion Using OpenCV 5. Number Plate Recognition Using SVM and Neural Networks 6. Non-rigid Face Tracking 7. 3D Head Pose Estimation Using AAM and POSIT 8. Face Recognition using Eigenfaces or Fisherfaces Index

Face tracking


The problem of face tracking can be posed as that of finding an efficient and robust way to combine the independent detections of various facial features with the geometrical dependencies they exhibit in order to arrive at an accurate estimate of facial feature locations in each image of a sequence. With this in mind, it is perhaps worth considering whether geometrical dependencies are at all necessary. In the following figure, the results of detecting the facial features with and without geometrical constraints are shown. These results clearly highlight the benefit of capturing the spatial interdependencies between facial features. The relative performance of these two approaches is typical, whereby relying strictly on the detections leads to overly noisy solutions. The reason for this is that the response maps for each facial feature cannot be expected to always peak at the correct location. Whether due to image noise, lighting changes, or expression variation, the only way...

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