Enforcing Convexity for Improved Alignment with Constrained Local Models.

Proceedings
Yang WangJeffrey Cohn

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 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.

Citations

Apr 15, 2015·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Pojala ChiranjeeviPratibha Moogi
Mar 10, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Pedro MartinsJorge Batista
Jan 23, 2016·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Shaoqing RenJian Sun
Dec 25, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Xiang YuDimitris N Metaxas
Jul 25, 2009·Language and Speech·Barry-John TheobaldSteven M Boker

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