Matthias Wimmer, Christoph Mayer, Freek Stulp, and Bernd Radig. Face Model Fitting based on Machine Learning from Multi-band Images of Facial Components. In Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, held in conjunction with CVPR, Anchorage, AK, USA, June 2008.
Geometric models allow to determine semantic information about real-world objects. Model fitting algorithms need to find the best match between a parameterized model and a gi ven image. This task inherently requires an objective function to estimate the error between a model parameterization and an image. The accuracy of this function directly inf luences the accuracy of the entire process of model fitting. Unfortunately, building these functions is a non-trivial task. Dedicated to the application of face model fitting, this paper proposes to consider a multi-band image representation that indicates the facial components, from which a large set of image features is computed. Since it is not possible to manually formulate an objective function that considers this large amount of features, we apply a Machine Lear ning framework to construct them. This automatic approach is capable of considering the large amount of features provided and yield highly accurate objective functions for fa ce model fitting. Since the Machine Learning framework rejects non-relevant image features, we obtain high performance runtime characteristics as well.