Authors:
Markus Storer
1
;
Peter M. Roth
1
;
Martin Urschler
1
;
Horst Bischof
1
and
Josef A. Birchbauer
2
Affiliations:
1
University of Technology, Austria
;
2
Siemens IT Solutions and Services, Austria
Keyword(s):
Active appearance model, Fast robust PCA, AAM fitting under occlusion.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Statistical Approach
Abstract:
The Active Appearance Model (AAM) is a widely used method for model based vision showing excellent results. But one major drawback is that the method is not robust against occlusions. Thus, if parts of the image are occluded the method converges to local minima and the obtained results are unreliable. To overcome this problem we propose a robust AAM fitting strategy. The main idea is to apply a robust PCA model to reconstruct the missing feature information and to use the thus obtained image as input for the standard AAM fitting process. Since existing methods for robust PCA reconstruction are computationally too expensive for real-time processing we developed a more efficient method: fast robust PCA (FR-PCA). In fact, by using our FR-PCA the computational effort is drastically reduced. Moreover, more accurate reconstructions are obtained. In the experiments, we evaluated both, the fast robust PCA model on the publicly available ALOI database and the whole robust AAM fitting chain on
facial images. The results clearly show the benefits of our approach in terms of accuracy and speed when processing disturbed data (i.e., images containing occlusions).
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