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Filtered Component Analysis to Increase Robustness to Local Minima in Appearance Models
F. De la Torre Frade, A. Collet Romea, J. Cohn, and T. Kanade
IEEE Conference on Computer Vision and Pattern Recognition, June, 2007.

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Appearance Models (AM) are commonly used to model appearance and shape variation of objects in images. In particular, they have proven useful to detection, tracking, and synthesis of people's faces from video. While AM have numerous advantages relative to alternative approaches, they have at least two important drawbacks. First, they are especially prone to local minima in fitting; this problem becomes increasingly problematic as the number of parameters to estimate grows. Second, often few if any of the local minima correspond to the correct location of the model error. To address these problems, we propose Filtered Component Analysis (FCA), an extension of traditional Principal Component Analysis (PCA). FCA learns an optimal set of filters with which to build a multi-band representation of the object. FCA representations were found to be more robust than either grayscale or Gabor filters to problems of local minima. The effectiveness and robustness of the proposed algorithm is demonstrated in both synthetic and real data.

Associated center: VASC
Associated project: Component Analysis for Data Analysis

F. De la Torre Frade, A. Collet Romea, J. Cohn, and T. Kanade, "Filtered Component Analysis to Increase Robustness to Local Minima in Appearance Models," IEEE Conference on Computer Vision and Pattern Recognition, June, 2007.

@inproceedings{De la Torre Frade_2007_5718,
   author = "Fernando De la Torre Frade and Alvaro Collet Romea and Jeffrey Cohn and Takeo Kanade",
   title = "Filtered Component Analysis to Increase Robustness to Local Minima in Appearance Models",
   booktitle = "IEEE Conference on Computer Vision and Pattern Recognition",
   month = "June",
   year = "2007"
}