Self-Adapting Feature Layers

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-by-synthesis 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 fully automated face reconstruction or recognition system. We analyze the robustness in a face recognition scenario on images from two databases: FRGC and FERET.