Facial feature extraction using a cascade of model-based algorithms

We present a cascaded framework for robust and accurate facial feature extraction. In this framework, we propose the following three model-based algorithms: (1) constrained global deformation using a sparse feature representation; (2) component texture fitting using direct parameter estimation by SVR, and (3) component feature refinement by direct optimization. The algorithms capture different characteristics of facial features, giving various extraction performances in terms of robustness (convergence) and accuracy. To achieve both high accuracy and robustness, we cascade these algorithms into a chain, where each algorithm progressively ´pulls´ the model closer to the correct position. Experiments show that the combined algorithm achieves a large convergence area and high accuracy.