Enforcing convexity for improved alignment with constrained local models

Constrained local models (CLMs) have recently demonstrated good performance in non-rigid object alignment/ tracking in comparison to leading holistic approaches (e.g., AAMs). A major problem hindering the development of CLMs further, for non-rigid object alignment/tracking, is how to jointly optimiz...

Full description

Saved in:
Bibliographic Details
Published in:2008 IEEE Conference on Computer Vision and Pattern Recognition Vol. 2008; pp. 1 - 8
Main Authors: Yang Wang, Lucey, Simon, Cohn, Jeffrey F.
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 23.06.2008
Subjects:
Active appearance model
Active noise reduction
Constraint optimization
Image databases
Lighting
Noise shaping
Optimization methods
Response surface methodology
Robots
Shape
ISBN:9781424422425, 1424422426
ISSN:1063-6919, 1063-6919
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Constrained local models (CLMs) have recently demonstrated good performance in non-rigid object alignment/ tracking in comparison to leading holistic approaches (e.g., AAMs). A major problem hindering the development of CLMs further, for non-rigid object alignment/tracking, is how to jointly optimize the global warp update across all local search responses. Previous methods have either used general purpose optimizers (e.g., simplex methods) or graph based optimization techniques. Unfortunately, problems exist with both these approaches when applied to CLMs. In this paper, we propose a new approach for optimizing the global warp update in an efficient manner by enforcing convexity at each local patch response surface. Furthermore, we show that the classic Lucas-Kanade approach to gradient descent image alignment can be viewed as a special case of our proposed framework. Finally, we demonstrate that our approach receives improved performance for the task of non-rigid face alignment/tracking on the MultiPIE database and the UNBC-McMaster archive.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISBN:9781424422425
1424422426
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2008.4587808