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Multistage feature extraction for accurate face alignment

We propose a novel multistage facial feature extraction approach using a combination of 'global' and 'local' techniques. At the first stage, we use template matching, based on an Edge-Orientation-Map for fast feature position estimation. Using this result, a statistical framework applying the Active Shape Model (ASM) is initialized and deformed to fit the real face image. In our proposal, we use a 2-D pattern search-and-fitting scheme guiding the deformation process, which provides more robustness and faster convergence than the traditional ASM. Our proposed approach for feature extraction shows good results dealing with a test set composed of faces images which are quite dissimilar with the faces used for the statistical training of the face model. The convergence area of our proposed technique almost quadruples compared to the ASM, while the amount of faces doubles for which the convergence is reached. The total processing for feature extraction takes less than 1 second for 250x250 face images on a Pentium-IV PC (1.7GHz).