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Enforcing Convexity for Improved Alignment with Constrained Local Models
Y. Wang, S. Lucey, and J. Cohn
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June, 2008.

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Abstract

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 efcient 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.

Notes

Associated center: VASC
Associated lab/group: Face Group
Associated project: Generic Active Appearance Models

Text Reference

Y. Wang, S. Lucey, and J. Cohn, "Enforcing Convexity for Improved Alignment with Constrained Local Models," IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June, 2008.

BibTeX Reference

@inproceedings{Wang_2008_6026,
   author = "Yang Wang and Simon Lucey and Jeffrey Cohn",
   title = "Enforcing Convexity for Improved Alignment with Constrained Local Models",
   booktitle = "IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)",
   month = "June",
   year = "2008"
}