Image Analysis for Marker-less Facial Tracking : Robust Face and Facial Feature Tracking Using AFM, AAA, VbTM and Post-processing

Wenlan Yang

ISBN 10: 3838339118 / ISBN 13: 9783838339115

Published by LAP LAMBERT Academic Publishing, 2010

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Tracking of facial features is increasingly used in game and film industry as well as for other applications. Most tracking systems are currently using markers which unfortunately are tedious and cumbersome. Marker-less facial tracking is supposed to eliminate the disadvantages of the marker-based approaches. This literature investigates different algorithms for marker-less tracking and presents how to apply them in a robust way. View-based and component sensitive normalized face images can achieve accurate tracking results based on the Active Appearance Algorithm. Post processing the parameters of global motions of the model smoothes the synthesized video sequence. Tacking results for faces and a tool developed for creating training images are also presented.

Seller Inventory # 9783838339115

Bibliographic Details

• Title: Image Analysis for Marker-less Facial Tracking : Robust Face and Facial Feature Tracking Using AFM, AAA, VbTM and Post-processing
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2010
• Language: English
• ISBN 10: 3838339118
• ISBN 13: 9783838339115
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 113 grams

About this title

Synopsis

Tracking of facial features is increasingly used in game and film industry as well as for other applications. Most tracking systems are currently using markers which unfortunately are tedious and cumbersome. Marker-less facial tracking is supposed to eliminate the disadvantages of the marker-based approaches. This literature investigates different algorithms for marker-less tracking and presents how to apply them in a robust way. View-based and component sensitive normalized face images can achieve accurate tracking results based on the Active Appearance Algorithm. Post processing the parameters of global motions of the model smoothes the synthesized video sequence. Tacking results for faces and a tool developed for creating training images are also presented.

About the Author

Wenlan Yang, MSc: Studied Computer Science at Uppsala University. Facial tracking developer at Imagination Studios AB in 2009, Uppsala, Sweden.