Self-Adapting Feature Layers

13 years 7 months ago
Self-Adapting Feature Layers
Abstract. This paper presents a new approach for fitting a 3D morphable model to images of faces, using self-adapting feature layers (SAFL). The algorithm integrates feature detection into an iterative analysis-bysynthesis framework, combining the robustness of feature search with the flexibility of model fitting. Templates for facial features are created and updated while the fitting algorithm converges, so the templates adapt to the pose, illumination, shape and texture of the individual face. Unlike most existing feature-based methods, the algorithm does not search for the image locations with maximum response, which may be prone to errors, but forms a tradeoff between feature likeness, global feature configuration and image reconstruction error. The benefit of the proposed method is an increased robustness of model fitting with respect to errors in the initial feature point positions. Such residual errors are a problem when feature detection and model fitting are combined to form a...
Added 08 Nov 2010
Updated 08 Nov 2010
Type Conference
Year 2010
Where ECCV
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