The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Non-rigid object alignment is especially challenging when only a single appearance template is available and target and template images fail to match. Two sources of discrepancy between target and template are changes in illumination and non-rigid motion. Because most existing methods rely on a holistic representation for the alignment process, they require multiple training images to capture appearance variance. We developed a patch-based method that requires only a single appearance template of the object. Specifically, we fit the patch-based face model to an unseen image using an exhaustive local search and constrain the local warp updates within a global warping space. Our approach is not limited to intensity values or gradients, and therefore offers a natural framework to integrate multiple local features, such as filter responses, to increase robustness to large initialization error, illumination changes and non-rigid deformations. This approach was evaluated experimentally on more than 100 subjects for multiple illumination conditions and facial expressions. In all the experiments, our patch-based method outperforms the holistic gradient descent method in terms of accuracy and robustness of feature alignment and image registration.

Keywords

Non-Rigid Object Alignment, Exhaustive Local Search

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Face Group and Component Analysis

Associated Project(s): Facial Expression Analysis and Generic Active Appearance Models

Number of pages: 8

Text Reference

Yang Wang, Simon Lucey, and Jeffrey Cohn, "Non-Rigid Object Alignment with a Mismatch Template Based on Exhaustive Local Search," IEEE Workshop on Non-rigid Registration and Tracking through Learning - NRTL 2007, October, 2007.

BibTeX Reference

@inproceedings{Wang_2007_5893,
   author = "Yang Wang and Simon Lucey and Jeffrey Cohn",
   title = "Non-Rigid Object Alignment with a Mismatch Template Based on Exhaustive Local Search",
   booktitle = "IEEE Workshop on Non-rigid Registration and Tracking through Learning - NRTL 2007",
   month = "October",
   year = "2007",
}

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