Parameterized Kernel Principal Component Analysis: Theory and Applications to Supervised and Unsupervised Image Alignment

Fernando De la Torre Frade and Minh Hoai Nguyen
IEEE Conference on Computer Vision and Pattern Recognition, July, 2008.


Download
  • Adobe portable document format (pdf) (396KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
Parameterized Appearance Models (PAMs) (e.g. eigentracking , active appearance models, morphable models) use Principal Component Analysis (PCA) to model the shape and appearance of objects in images. Given a new image with an unknown appearance/shape configuration, PAMs can detect and track the object by optimizing the model's parameters that best match the image. While PAMs have numerous advantages for image registration relative to alternative approaches, they suffer from two major limitations: First, PCA cannot model non-linear structure in the data. Second, learning PAMs requires precise manually labeled training data. This paper proposes Parameterized Kernel Principal Component Analysis (PKPCA), an extension of PAMs that uses Kernel PCA (KPCA) for learning a non-linear appearance model invariant to rigid and/or non-rigid deformations. We demonstrate improved performance in supervised registration, and present a novel application to improve the quality of manual landmarks in faces. In addition, we suggest a clean and effective matrix formulation for PKPCA.

Keywords
image aligment, kernel methods, principal component analysis

Notes
Note: the associated project is component analysis for data analysis and face group

Text Reference
Fernando De la Torre Frade and Minh Hoai Nguyen, "Parameterized Kernel Principal Component Analysis: Theory and Applications to Supervised and Unsupervised Image Alignment," IEEE Conference on Computer Vision and Pattern Recognition, July, 2008.

BibTeX Reference
@inproceedings{De_la_Torre_Frade_2008_6038,
   author = "Fernando {De la Torre Frade} and Minh Hoai Nguyen",
   title = "Parameterized Kernel Principal Component Analysis: Theory and Applications to Supervised and Unsupervised Image Alignment",
   booktitle = "IEEE Conference on Computer Vision and Pattern Recognition",
   month = "July",
   year = "2008",
   Notes = "the associated project is component analysis for data analysis and face group"
}